Staphylococcus aureus is a major human pathogen and an important cause of livestock infections. More than 20 staphylococcal enterotoxins with emetic activity can be produced by specific strains responsible for staphylococcal food poisoning, one of the most common food-borne diseases. Whole genome sequencing provides a comprehensive view of the genome structure and gene content that have largely been applied in outbreak investigations and genomic comparisons. In this study, six enterotoxigenic S. aureus strains were characterised using a combination of molecular, phenotypical and computational methods. The genomes were analysed for the presence of virulence factors (VFs), where we identified 110 genes and classified them into five categories: adherence (n = 31), exoenzymes (n = 28), genes involved in host immune system evasion (n = 7); iron uptake regulatory system (n = 8); secretion machinery factors and toxins’ genes (n = 36), and 39 genes coding for transcriptional regulators related to staphylococcal VFs. Each group of VFs revealed correlations among the six enterotoxigenic strains, and further analysis revealed their accessory genomic content, including mobile genetic elements. The plasmids pLUH02 and pSK67 were detected in the strain ProNaCC1 and ProNaCC7, respectively, carrying out the genes sed, ser, and selj. The genes carried out by prophages were detected in the strain ProNaCC2 (see), ProNaCC4, and ProNaCC7 (both positive for sea). The strain ProNaCC5 resulted positive for the genes seg, sei, sem, sen, seo grouped in an exotoxin gene cluster, and the strain ProNaCC6 resulted positive for seh, a transposon-associated gene. The six strains were used for the production of naturally contaminated cheeses which were tested with the European Screening Method for staphylococcal enterotoxins. The results obtained from the analysis of toxins produced in cheese, combined with the genomic features represent a portrait of the strains that can be used for the production of staphylococcal enterotoxin-positive cheese as reference material. The advances in sequencing technologies, including next generation sequencing (NGS) have contributed to the revolution of food safety, facilitating the detection and characterisation of foodborne pathogens ,2,3. SinWhole genome sequencing (WGS) provides a comprehensive view of the genome structure and gene content of microorganisms ,15,16, aS. aureus (S. aureus) strains that may produce one or more heat-stable enterotoxins in food, causing vomiting and abdominal pain in humans [One of the most common foodborne related diseases is the staphylococcal food poisoning (SFP), resulting from the consumption of foods containing sufficient amounts of staphylococcal enterotoxins (SEs) . SFP is n humans . In 2015n humans , indicatn humans .S. aureus VFS are carried by mobile genetic elements (MGEs) and consist of prophages, plasmids, transposons, and S. aureus pathogenicity islands (SaPIs) [S. aureus strains were genome-sequenced. A report of their virulence portraits is presented, and the databases used for the detection of the genomic features are described. The strains were also used for the production of naturally contaminated cheeses, suggesting new opportunities for the production of reference materials for inter-laboratory tests, required by regulations [To date, 26 different staphylococcal superantigens (SAgs) have been identified, including the staphylococcal enterotoxins  reported as responsible agents for foodborne outbreaks ,28,29; t (SaPIs) ,43, whic (SaPIs) ,44. MGEs (SaPIs) . In thisulations . The resS. aureus which were maintained in cryogenic vials stock culture beads at −80 °C in two collections: at the European Reference Laboratory for Coagulase Positive Staphylococci,  and at the Italian Reference Laboratory for CPS . Six S. aureus strains were chosen for this study, representing enterotoxin producers isolated from cheese (five strains) and one strain isolated from a composed salad. Among the six strains, three were isolated in cheeses, which resulted as being responsible for foodborne poisoning involving patients   and incubated statically for 24 h at 37 °C. A total of 10 µL were plated on tryptone soya agar (TSA)  and incubated for 24 h at 37 °C for checking purity and obtaining single colonies. Species confirmation was performed by a VITEK system GP card . Single colonies were re-suspended in TSB and cultured for 18 h at 37 °C and 1.8 mL of the pure cultures were used for DNA isolation with DNeasy UltraClean Microbial Kit  following the manufacturer’s protocol. Quality and DNA concentrations were determined with a Qubit 2.0 fluorometer  and Agilent 2100 bioanalyzer .sea to see and ser for the first mPCR  and from seg to selj and ser for the second mPCR ; the primers used are reported in S. aureus strains were used as positive controls for the SEs genes: S. aureus FRIS6 , S. aureus FRI137 , S. aureus FRI326 (positive for see), S. aureus FRI361 , and S. aureus HMPL280 .The strains were analysed for the presence of 11 enterotoxins using 2 multiplex PCR assays (mPCR) according to the EURL CPS methods ,50. The S. aureus isolates targeting seven housekeeping genes  according to Enright et al. [spa typing following the method developed by Harmsen and colleagues [Multilocus sequence typing (MLST) was performed on t et al. . Strainslleagues .S. aureus, according to the manufacturer’s instructions. The six strains were analysed for the presence of genes mecA and mecC (methicillin resistant S. aureus factors—MRSA) along with the genes pvl  and spa, according to the method by Stegger et al. [To determine the susceptibility to antimicrobials, Vitek 2  testing was performed using software version 5.04 and the AST-GP79 cards for r et al. .De-novo contigs were generated using SPAdes (v.3.9.1) [Genomic libraries were prepared using the Illumina Nextera library preparation kit for each strain following the manufacturer’s protocol. WGS was carried out on the Illumina MiSeq platform. The quality of the reads was assessed using FastQC (version 0.11.5) . Specifiv.3.9.1) , and thev.3.9.1) . The assv.3.9.1)  and RASTv.3.9.1)  for the http://patricbrc.org) (v3.5.41) [https://github.com/tseemann/abricate) interrogating the comprehensive antibiotic resistance database (CARD) (https://card.mcmaster.ca). ABRicate was used for the creation of three novel databases built for the detection of SaPIs (http://enve-omics.ce.gatech.edu/ani/index) and EzGenome (http://www.ezbiocloud.net/ezgenome/ani). The genomes of 29 enterotoxigenic S. aureus strains were used for the genomic comparison of the six strains presented in this study and included in the constructing of the pan-genome, which was annotated with the use of the Clusters of Orthologous Groups (COGs) database [https://www.ncbi.nlm.nih.gov/assembly/), and the details of the strains are reported in the (R Core Team 2018). For the construction of the approximate maximum likelihood phylogenetic tree, FastTree was used [The genomes were interrogated for a pool of 1300 genes, including VFs reported for staphylococci and determined using a combination of a database built for this study  and the v3.5.41) . The VFsof SaPIs , plasmiddatabase . The genwas used . In addig at 10 °C, and the supernatant and cell debris were removed by filtrationwith 0.22 µm filters. SEs detection was performed with the use of commercially available polyvalent enzyme-linked immunoassay (ELISA) RIDASCREEN SET Total  and enzyme-linked fluorescent immunoassay (ELFA), Vidas SET2 . These reference methods can simultaneously detect SEA to SEE in food matrices without differentiating the five SEs [The strains used in this study were tested for the production of SEs in broth culture. Briefly, two colonies from TSA were transferred to 10 mL of TSB and statically incubated at 37 °C for 18 h. The cultures in broths were centrifuged for 5 min at 3500× five SEs .8 CFU/mL. The milk was incubated for 18 h at 37 °C, allowing the strain to produce the enterotoxins. After incubation, CPS were enumerated according to ISO 6888-2:1999 [The six strains selected for this study were used for inoculating bovine pasteurised whole milk  followed by the production of 12 batches of cheese (two for each strain). For obtaining a batch of SE-positive cheese, each TSA pure culture of the strains were re-suspended in 30 mL of sterile 0.85% NaCl solution obtaining a 3 McFarland unit suspension that was inoculated into 10 L of milk, corresponding to 9 × 108-2:1999 . The milS. aureus ProNaCC1, ProNaCC2, ProNaCC4 and ProNaCC6 resulted positive for sed, see, sea, and seh, respectively. S. aureus strain ProNaCC5 resulted positive for seg and sei, and ProNaCC7 was positive for the genes sea, sed, selj, and ser  but different spa-type . All the strains resulted negative for the genes mecA, mecC and pvl, and were, therefore, predicted as methicillin-sensitive S. aureus (MSSA). Four of the strains resulted positive for one staphylococcal enterotoxin gene by mPCR:  and ser . The spaspa type therefore, both the forward and reverse sequences of the gene spa were submitted to the server https://spa.ridom.de/index.shtml and a unique repeat succession was detected, assigned to the new spa type t19075.In addition, the strain ProNaCC4 resulted a new The strains resulted sensible to all the antimicrobials tested in this study except for enrofloxacin for which all the strains were resistant at a concentration of ≤0.5 mg/L. The strains ProNaCC1 and ProNaCC6 resulted resistant for benzylpenicillin at a concentration of ≥0.5 mg/L . The MICS. aureus strains were whole-genome sequenced, and the draft assemblies ranged in size from 2.2 to 2.8 Mb with a range of 2126 to 2747 CDs , exoenzymes (n = 29), genes involved in host immune system evasion (n = 7); iron uptake regulatory system (n = 8); secretion machinery factors and gene of toxins (n = 36) .S. aureus strains related to adherence factors. The genes identified in this category were: autolysin (atl), genes involved in binding proteins and adhesion , clumping factor genes , and cell wall surface anchor protein (sasG)  .atl, icaA-C, D, R, and sdrC, E were detected in all the strains; all the strains have at least one gene associated to bacterial proteins with the function of binding the major components of the extracellular matrix . The gene cna coding for collagen adhesin was detected only in the strains ProNaCC2 and ProNaCC6.The genes coa), serine protease family (splA-F), staphylokinase (sak), and other genes coding for enzymes  and the thermonuclease (nuc) genes were found in all the strains.All the genes coding for extracellular proteases were found in the six isolates, except for cap8A_1 and 2), immunoglobulin-binding protein (sbi) immunoglobulin G-binding protein A (spa) and the staphylococcal complement inhibitor (scn)  .cap (cap8A_1-2) and scn (scn_1-3) and spa were detected on all the strains. The gene sbi was found, except in the strain ProNaCC5.The genes isdA, isdB, isdC, isdE, isdF, isdG, isdH, and isdI) were identified  and leucotoxic activities , the genes involved in the secretion machinery , the toxic shock syndrome toxin (tst), the exfoliative toxin type b (etb) and enterotoxins  were detected (In this class the genes for toxins with haemolytic (detected .essD) [essD and essC, which are required for the secretion of substrates including EsxA and EsxB [hlgA, hlgB, hlgC and hly) were identified in all the strains, including the β-hemolysin (hlb_1) which was found in all the strains except in the strain ProNaCC4 and ProNaCC6 where it was found the gene hlb_2 was found, which is the coding gene for a truncated protein. Different genes related to components of Leucotoxins were found in all the strains, in particular, the genes lukEV and lukDV coding for the Leucotoxin LukEv and LukDV, respectively. All the genes coding for the secretion machinery were detected in the strains ProNaCC1, ProNaCC6 and ProNaCC7. The strains ProNaCC6 and ProNaCC4 had all the genes for the secretion machinery, except the nuclease effector (essD) , the strand EsxB . Among hentA, entD, entE, entG, entH) or for protein precursors  and the exfoliative toxin type B (etb) was detected only in the strain ProNaCC4.All the strains had at least one gene coding for enterotoxins either with intact sequences  were grouped in a sixth group . agrA, garL, graS_1, graS_2, lytR, pyrR, saeR, saeS, sarA, sarR, sarS, sarV, sarX, sarZ, srrA, srrB tarL, vraR, vraS walk, walR, ypdA, yycHm and yycI were detected in all the genomes. The genes involved in the arsenical resistance operon repressor (arsR) were detected in the strains ProNaCC1 and ProNaCC7, and cadmium resistance transcriptional regulatory protein (cadC) was detected in the strains ProNaCC1, ProNaCC5, ProNaCC6, and ProNaCC7, and the transcriptional repressor that constitutively blocks the expression of β-lactamase, bla family  was detected in the strains ProNaCC1 and ProNaCC7. The genes coding the S. aureus two-component systems (srrA-srrB and arlR-arlS) involved in the global regulation of VFs, were detected in all the strains except in the genome of the strain ProNaCC5.In this category, a total of 39 genes were identified and, selected for their involvement as transcriptional regulators of staphylococcal virulence factors the genes Further analyses were performed for the gene coding staphylococcal enterotoxins. A novel database was built for ABRicate starting from amino acid sequences extracted from GenBank nucleotide and uniprot databases . The seqThe genomes were submitted to the PHASTER web server for the identification of prophage sequences and the output was tested for the presence of SEs with the database developed for ABRicate. The intact prophages regions and the hypothetical proteins sequences associated with prophages were considered in the study for the analysis of SEs similarities. According to the score assigned by the software, 20 of the prophages most common regions within the six enterotoxigenic genomes were identified: three regions resulted intact, 13 regions were incomplete, and four were questionable .The intact prophage most common regions were identified on the strain ProNaCC2 , ProNaCC4 , and ProNaCC7 . The phage regions included the gene for the staphylococcal enterotoxin type E in the strain ProNaCC2 and the gene for the staphylococcal enterotoxin type A in the strains ProNaCC4 and ProNaCC7 .tst locus was further investigated for the genomic localisation, because this virulence factor can be associated with SaPIs, which includes the genes for specific staphylococcal enterotoxins. The database developed for this analysis was built for ABRicate and had sequences of 74 SaPIs included the specific sequences of SEs found in these loci  [S. aureus genomes that had a sequence identity at the nucleotide level of 100% with the reference sequence of the bagel3 database. The sequence was found in the genome of the strains ProNaCC1, ProNaCC6, and ProNaCC7. The second highest percentage identity bacteriocin gene was the delta-hemolysin (hld), which had 80.00% of identity with the reference sequence, and it was found in the genome of ProNaCC1, ProNaCC4, ProNaCC5, and ProNaCC7. In total, 38 other bacteriocins (The lanthionine-containing peptide antibiotic (lantibiotic) gene (bsaA2) , was theeriocins  with a sA pan-genome of 5651 genes was generated from the six enterotoxigenic strains and 29 enterotoxigenic strains reported in the literature . The totselx and selw were the most prevalent among the 39 strains; nevertheless, no correlation was found between the country of isolation and origin of the samples. The functional annotation of the six-enterotoxigenic S. aureus pan-genome was 66.2% of the COGs database with known functions of the total pan-genome  was detected in the core genome, as already described, along with the gene selw, in almost all the 35 strains included in the pangenome study. However, the genes coding for enterotoxins were detected mostly in specific accessory genomic regions, demonstrating the importance and the role of these genomic structures for the evolution of S. aureus as foodborne pathogens.The pangenome of the six enterotoxigenic The strains ProNaCC1, ProNaCC2 and ProNaCC4 produced SED, SEE and SEA in 0.85% NaCl solution respectively. The strains ProNaCC5 and ProNaCC6 resulted negative for the production of toxins in vitro and the strain ProNaCC7 produced both SEA and SED. The milk used in this study had a fat content of 36.0 g per litre, 49.0 g of carbohydrates per litre, 32.0 g of proteins per litre, and 0.9 g of NaCl per litre as the average content declared by the producer. Four batches of the milk incubated with the enterotoxigenic strains (18 h) resulted positive at both qualitative methods for SEs detection  for the 7 and 1.1 × 108 CFU/g. The fresh cheeses were tested for CPS after 3 h of ripening (sweating stage) and their number was between 2.5 and 5.6 × 108 CFU/g  locus consists of different genes  and the promotor icaR, which are involved in the biofilm formation initiated by atl [ica-positive were isolated in both the food and clinical environment, but their presence was not always associated with the production of biofilm [aap) and the biofilm-associated protein (bap), but none of the six enterotoxigenic strains resulted positive. Other genes associated with adhesion, invasion, and evasion were found in all the strains, in particular, the genes sdrC and sdrE, which were reported in clinical isolates [The virulence of adhesins . In the d by atl ,88. In o biofilm . The datisolates ,91.S. aureus can produce exoenzymes which are active against the immune system and have a proteolytic effect on complex molecules. The genes coding for thermonuclease (nuc), aureolysin (aur), staphylocoagulase (coa), lipase (lip), and extracellular matrix protein-binding protein (ssp) were found in all the strains; this result was demonstrated in other studies for non-aureus staphylococci, where these genes were the most reported among this group of VFs [splA-F) are organized in an operon and exhibit proteolytic activity [S. aureus isolates yet. Only the strains ProNaCC4 and ProNaCC7 had the gene for staphylokinase  (sak). This virulence factor is located in different prophages, including the prophages carrying the genes for the enterotoxin SEA [Enterotoxigenic p of VFs ,92. The p of VFs . The genactivity . At leasoxin SEA , which wS. aureus is related to the ability to evade the host immune system and to produce capsular polysaccharides, including the enterotoxigenic strains [sbi, scn and spa) were detected; the presence of these genes can be an advantage for pathogenesis of these enterotoxigenic strains as suggested in different studies where the genes were considered highly specific for staphylococcal isolates of human origin, including methicillin-resistant and methicillin-sensitive S. aureus [The virulence of  strains . Among t. aureus .isd). This group of genes is encoded by five transcriptional units including isdA, isdB, isdC-D-E-F-srtB-isdG, isdH, and isdI [isdH that was not detected in the strains ProNaCC2, ProNaCC4 and ProNaCC5. The accessory gene regulator agr is a S. aureus quorum-sensing system involved in the control of different VFs including toxins [agr was detected in all the strains within the genes for SarA protein family, SrrAB protein, and SaeRS two-component system that controls the expression of several virulence factors with both positive and negative effects. The genes coding for the member of the two-component regulatory system WalK/WalR, VraS/VraR and the genes of the YycH family protein were found in all the six enterotoxigenic strains. These factors are involved in the control of autolysis, biofilm formation and cell wall metabolism [The VFs involved in the uptake of iron and metabolism found among the six enterotoxigenic strains were part of the iron-regulated surface determinants  previously described as a versatile secretion system in S. aureus that might be involved in the transport of protein and or DNA [S. aureus, and the enterotoxin selX was detected as a component of the core genome [S. aureus strains, according to a study that included strains isolated in the case of human and animal infections [S. aureus acquires VFs encoded in the mobile genetic elements such as prophages but also plasmids, pathogenicity, and genomic islands, providing adaptation but also a genetic rearrangement resulting in hypervirulence of some strains but also the loss of portion of genes [entA_1-3 were found in the genome of all the strains  were identified in the genome of the strain ProNaCC7 and had an 85.14% of coverage and 99.83% identity with the reference sequence (GQ900447.1)  were found within the sequences of the plasmids confirming their origin from mobile genetic elements. The genes for the plasmid encoded enterotoxins SED, SElJ and SER were found in the genome of the strains ProNaCC1 and ProNaCC7. The plasmid sequences found in the genome of the strain ProNaCC1 had a 98.11% coverage and 99.78% identity at the nucleotide level with the plasmid pLUH02 (accession number NZ_LJBK01000031.1) , which w00447.1) . The gensed and selj, which are carried out in pIB485 and the gene selj and ser carried out in pF5, while both the strains (ProNaCC1 and ProNaCC7) carried out the sequences of the genes sed, selj, and ser in a unique putative plasmid region. In addition, the plasmid encoded genes involved in the arsenical resistance operon repressor (arsR) was detected in the two strains  and pF5 (accession number AB330135.1) reported in strains responsible for food poisoning ,103,104. strains  along wins  and the staphylococcal enterotoxin-like genes . In seg, seh, sei, or selj [splC, splE and splF) in addition to the SEs/SEl genes [splE, suggesting a possible variant of this genomic island.The strain ProNaCC5 resulted positive for several non-classical staphylococcal enterotoxin genes , suggesting a role of the gene in the resistance mechanism. On the other hand, the strain ProNaCC7 resulted sensible at low concentrations of the antibiotic (0.06 g/L)  , and theS. aureus was assembled and applied to highlight the genetic, metabolic, and pathogenic diversity of the species in comparison with other 29 enterotoxigenic strains. Consistent with prior studies, the numbers of new genes per genome indicated substantial intraspecific variation and overall pangenome “openness” associated with a broad niche range [S. aureus is a versatile microbe in complex microbial communities both in the environment or in a specific host, shifting between commensal or pathogenic variant [S. aureus is a clonal species [S. aureus isolates can have significant effects on virulence, proliferation, and persistence [selx) was found in the core genome of the six enterotoxigenic strains; this result is comparable with another study where the gene was found in the core genome of 95% of a diverse S. aureus population, including isolates from human and animal infections [S. aureus genomes of isolates that were considered enterotoxin-producers in addition to reference strains used for the genome comparison were included. Interestingly, it was possible to identify other genes coding for enterotoxins in addition to the genes that were reported in the literature, especially novel enterotoxins that were not genetically characterized at the time of the publication of the sequences. This result underlines the importance of a novel bioinformatics approach for typing the enterotoxins which can be produced by S. aureus, and their use as a tool in support of diagnostics.The pangenome of the six enterotoxigenic he range ,111 but he range . In fact variant ,114. Fun species . Recent sistence . Thus, cfections . The othS. aureus strains were produced. The cheesemaking, followed a protocol that was published before by Bianchi et al. [S. aureus cells or their survival during the ripening were not recorded. The enumeration of coagulase positive staphylococci (CPS) resulted high after the sweating process, between 2.5 and 5.6 × 108 CFU/g. In fact, according to the EC Regulation 2073/2005 on microbiological criteria for foodstuffs, when CPS enumeration exceeds 105 CFU/g or CFU/mL, the samples must be tested for enterotoxins due to their presumed presence if the CPS population is constituted by enterotoxigenic strains. SEs detection methods rely on commercially available polyvalent enzyme-linked immunoassays (ELISA) or enzyme-linked fluorescent immunoassay (ELFA) with antibodies that detect SEA-SEE and the cheese samples spiked with the strain ProNaCC1-4 and ProNaCC7 resulted positive for at least one SE  could have caused the SFP as reported in previous studies [As demonstrated in different studies, food-related stress conditions, such as NaCl, nitrite, glucose and lactic acid variations encountered during food production and preservation, can induce significant changes in the activity of SEB and SED promoter genes ,118. In i et al.  for the t one SE . The strt one SE , and res studies  supportiS. aureus used for the production of naturally-contaminated cheeses, and in the future, the identification of SEs and the development of innovative computational approaches will allow more accurate identification of additional genetic factors and genes necessary for the production of enterotoxins in food.This study was limited to the genomic and in-silico prediction of the presence of VFs and SEs. Future developments and validation of methods for the detection of not-classical SEs and proteomic approaches will allow for a better understanding of in vivo production of the enterotoxins. The strains were whole-genome sequenced and were detected virulence factors, antimicrobial resistance genes, and mobile genetic elements with a specific focus on the staphylococcal enterotoxin and enterotoxin-like genes. Using novel databases offers a comprehensive portrait of enterotoxigenic strains of The data have been deposited as a BioProject PRJNA419794 in the National Center for Biotechnology Information. The SRA are available at the following accession numbers: SRX3422350, SRX3422349, SRX3422348, SRX3422347, SRX3422346, SRX3422345.


Microalgae are photosynthetic microorganisms and are considered excellent candidates for a wide range of biotechnological applications, including the removal of nutrients from urban wastewaters, which they can recover and convert into biomass. Microalgae-based systems can be integrated into conventional urban wastewater treatment plants (WW-TP) to improve the water depuration process. However, microalgal strain selection represents a crucial step for effective phytoremediation. In this work, a microalga isolated from the effluent derived from the thickening stage of waste sludge of an urban WW-TP was selected and tested to highlight its potential for nutrient removal. Ammonium and phosphate abatements by microalgae were evaluated using both the effluent and a synthetic medium in a comparative approach. Parallelly, the isolate was characterized in terms of growth capability, morphology, photosynthetic pigment content and photosystem II maximum quantum yield. The isolated microalga showed surprisingly high biomass yield and removal efficiency of both ammonium and phosphate ions from the effluent but not from the synthetic medium. This suggests its clear preference to grow in the effluent, linked to the overall characteristics of this matrix. Moreover, biomass from microalgae cultivated in wastewater was enriched in photosynthetic pigments, polyphosphates, proteins and starch, but not lipids, suggesting its possible use as a biofertilizer. Chlorella, Scenedesmus, Chlorococcum, Botryococcus, Nannochloropsis and Neochloris [2 to reduce inorganic nutrients to organic matter, i.e., biomass, and are O2-evolving organisms, thus mitigating the negative impact of CO2 emissions, especially in high CO2-emitting systems such as industries and WW-TPs [2 mitigation and metal reduction can take place at the same time; 5. the effluent discharged in the water bodies is oxygenated [The rise in the world population and the increasing use of natural resources which have occurred during the last century have caused strong alterations to the environment. As it is known, the world overpopulation is gradually increasing the demand for freshwater, especially in cities, also leading to a consequent increase in wastewater production ,2. Urbanochloris ,13,14,15ochloris ,13,16. Md WW-TPs ,17. On tMicroalgae are regarded as very good candidates for their application in WW-TPs as tertiary phytoremediation treatments of urban wastewater (UWW) effluents and for natural resource preservation, thanks to recycling processes in a “waste-to-value” approach. As aforementioned, microalgal biomass is proposed for several uses  ,23,24,25The choice of the most suitable microalgal organism is a crucial step in developing an effective microalgae-based tertiary treatment inside WW-TPs. Many studies have focused on the selection of the best candidates among algal strains from culture collections for this application ,9,13,15,Among UWW effluents from WAS treatments, much attention has been directed towards the employment of centrate streams after the dewatering process or of the surnatant from the anaerobic digestion ,40,42,43V/FM ratio) and photosynthetic pigment content; total protein content was also determined in the final harvested biomass after the phytoremediation test with the UWW effluent. In parallel, N and P removal from the synthetic media or UWW was monitored.Therefore, the aim of this work was to isolate promising autochthonous microalgae from the effluent derived from the thickening step of the WW-TP located in Ferrara  to be employed in an eco-friendly phytoremediation treatment to improve the performance of the WW-TP. Ferrara is the largest city immediately next to the delta of the Po River and its regional park, which include inland and coastal wetlands of high conservation value; thus, more efficient functionality of the wastewater depuration process as a whole can represent a benefit also for the protection of the environment of such an important area. On the other hand, the use of microalgae isolated from the thickening stage of WAS treatment can also be extended to other contexts with similar problems and wastewaters. The study was planned to test algal growth and nutrient removal from the UWW effluent using the most promising microalga among those isolated or collected from the stream. For the research, microalgae were studied through morphological observations , growth analyses , maximum efficiency of the photosystem II (PSII) quantum yield  and shapes , the most characteristic being reported in Among the microalgae described above, consortia from the upper layer and the small spherical microalgae in the lower one were the most dominant and representative algae grown in the sewage. Thus, they were selected to be studied also at the ultrastructural level to better understand their morphology and potential for nutrient removal. TEM observations highlighted that microalgae in the consortia were featured as: 1. small cells of 2–3 µm, containing a large chloroplast with evident pyrenoid a; 2. larChlorella-like genus, due to morphological characteristics, such as the chloroplast feature with a large pyrenoid [Differently, the dominant algal form harvested from the lower layer was formed, as expected, by homogeneous populations constituted of only small cells (around 2–3 µm) f,g, simipyrenoid ,47,48. IChlorella-like cells as a monoculture free of bacterial contaminants. This monoculture was selected for further studies.Among the microalgal material grown in the effluent, subsequent cultivation in Petri dishes allowed the isolation of the 6 cells mL−1 at the 7th day of experiment (vs 4 × 106 cells mL−1 for controls in synthetic BG11 medium). Subsequently, from 7 to 14 days of cultivation, a short and transient stationary phase was observed for both UWW and BG11 algae. In both cases, the temporary stationary phase was followed by a small rise in growth, which resulted in a final cell density of around 8 and 20 × 106 cells mL−1, respectively, for samples in synthetic medium and in UWW. On the whole, two growth phases characterized the cultures: a first, evident one from 0 to 4 days for treated algae and from 4 to 7 days for controls , and a second, residual one from 14 days to 21 days (µ = 0.09−0.08 day−1). Based on these differences, the cell density of algae cultivated in the sewage was always significantly higher than in the control synthetic medium (around 2.5 times at the end of experiment). In parallel, the dry biomass data gave results in agreement with the observed growth kinetics, but a more emphasized difference between control and UWW-treated samples was observed .During the 21-day experiments, BG11 media did not undergo relevant removal of nutrients, with a percentage removal efficiency (RE) of only around 8% for ammonium and no abatement for phosphates. Only a transitory removal was observed during the first periods of cultivation, with a RE of 19% for ammonium from 4 to 7 days of cultivation and of 29% for phosphates from 0 to 4 days a,b. Diff3−, in BG11-modified media, it remained at trace concentrations (below 0.05 ppm) (Concerning N-NO.05 ppm) c. Instea4+ in the same volume of UWW. Considering a final residual amount of 2.8 mg of N-NH4+ , the largest amount of N (61%) was assimilated by the algae, while the missing 24.5% was stripped in the atmosphere  corresponded to an estimate of around 11.6 mg of N fixed in a 300 mL culture of the alga, which started from 19.1 mg of N-NHmosphere d.Cultivation of the microalgal isolate in UWW or in modified BG11 did not cause noticeable differences in the cell morphology, since the algae maintained an overall spherical shape, almost similar size (3–5 µm) and a chloroplast with an evident pyrenoid . Howevera, Chl b and carotenoids (Cars) were more accumulated inside algae cultivated in UWW than in BG11. In particular, trends in concentrations of both Chls were substantially identical between BG11 controls and UWW-treated samples, but soon after 4 days of cultivation, the cells in the effluent reached higher pigment concentrations than in synthetic medium (from +75% at day 4 up to 2.2 times more at the end of the experiment). Differently, for Car content, differences were most conspicuous at 4–7 days of cultivation, when algae in UWW showed a Cars concentration that was doubled in comparison to BG11  of algae remained quite similar in all samples or even slightly higher in UWW samples than in BG11  and therefore it was not used for subsequent studies in the present research. Additionally, the consortium was not the most representative algal population in the UWW. Differently, the most abundant alga grown in the UWW was successfully isolated, leading to a monoculture of Chlorella-like chlorophyte, which was selected for further studies, even though it did not show apparent morphological characteristics that could suggest an alga capable of accumulating P when cultivated in synthetic medium. In a very attractive way, this isolate gave instead excellent results, both in terms of growth and nutrient removal from the UWW but also of the accumulation of biotechnologically valuable molecules.In the present paper, microalgae were isolated or collected from streams of the thickening step of WAS. It is now widely recognized that, compared to microalgae from collections, algae directly isolated from wastewater are potentially better suited to grow in it, exploiting the mineral component for growth and being already adapted to the toxic elements that they may contain  ,38,40. HComparing results from cultivations in BG11-modified synthetic medium and in UWW, there is no doubt that the alga clearly preferred the effluent and thus the matrix from which it was isolated. This is evident not only by considering growth and morpho-physiological aspects but also nutrient removal capability.−1), nine had lower (below 0.3 day−1) and only three higher values (0.7 day−1). In our work, the effective growth of algae cultures in UWW testifies that N and P content only partially contributed to sustaining the growth of the microalgae. It is plausible that micronutrient composition, together with organic matter responsible for COD and BOD-5 , could have contributed to enhancing the growth of the microalgae in the UWW. Organic matter associated with COD (or BOD-5) is in fact usable for microalgal growth, so much so that microalgae-based systems are proposed for the concomitant removal of N and P and the reduction of COD (or BOD-5) in the depuration treatment of wastewaters, including waste effluents recirculating inside urban WW-TPs [C. vulgaris or Galdieria sulphuraria, in wastewater treatment tests [2 and carbonates), then used by the microalgae for photosynthesis, as also described in [2. Overall, the C:N:P stoichiometry ratio in UWW was clearly more suitable for algal growth than that in the BG11-modified control medium, where C appears to be the limiting factor. In addition, concentrations of other macronutrients and of micronutrients, such as Cu, Mg, K, Mn, Fe, Ca, are never limiting for microalgal growth in effluents [−1, thus similarly abundant as in BG11 (0.02 mg L−1)   and suffIsochrysis galbana, the reduction in Chls content in P-limited microalgal cultures cannot be attributed to a nutrient recycling metabolism but to an incapability of synthesizing the ATP required for cell metabolism, including the biosynthesis of Chls [V/FM ratio. Moreover, higher values of the aforementioned photosynthetic parameters obtained for UWW algae could represent a further clue supporting the mixotrophic metabolism of the algal isolate in the effluent, as similar results were obtained for a well-known mixotrophic green microalga, N. oleoabundans, cultivated with a pure organic carbon substrate [2, which is then used for photosynthesis [2 than used for photosynthesis, and this resulted in the gradual pH decrease.Even if P is not a constituent of Chls, its presence in culture media positively impacts the biosynthesis of these pigments as well as the entire energetic metabolism inside the cells . As repo of Chls ,68. In oubstrate ,69. Undeynthesis . The incBesides pigments, cells grown in the UWW also contained high amounts of other interesting compounds, such as starch and proteins, but not lipids. Ultrastructural observations showed that the algae in the effluent accumulated somehow more starch than control cells in the BG11 synthetic medium; accordingly, pyrenoids in UWW-treated algae were slightly larger than in controls. Differently, neither in the effluent nor in BG11 did the algae accumulate lipids, as revealed by ultrastructural analyses. This is not so surprising, since it is well recognized that lipids are accumulated inside cells in response to nutrient starvation , a condi4+ and P-PO43−, respectively, in 21 days). In parallel, the comparatively modest growth of algae in modified BG11 control medium did not cause any evident nutrient removal.As regards the evaluation of nutrient removal by the microalgal isolate from the effluent or from synthetic media, the very high growth of microalgae in UWW cultures was linked to the conspicuous ammonium and phosphate abatement from the medium  result in ammonium stripping or phosphate precipitation [For P abatement in the UWW effluent, a luxury P uptake mechanism can be proposed, as large polyphosphate depots were formed during cultivation. Luxury P uptake is intended as the strategy that makes algae able to assimilate more P than that needed for immediate growth through the formation of inorganic polyphosphate granules ,72. Thesinterval . FurtherO3− ions ,79. Diffpitation ; this li4+ reduction observed in UWW, but not in BG11 samples, could be very likely attributed to nitrifying bacteria present only in the UWW. Despite its quantitatively negligible presence in the effluent, N-NO3- content was measured to possibly highlight interactions between microalgae and bacteria naturally resident in the UWW, so interactions were certainly absent in the controls cultivated in bacteria-free synthetic BG11 medium. Results showed that in UWW samples, the unexpectedly increased concentration of N-NO3− could be due to some nitrifying bacteria still present in the effluent and able to convert ammonium into nitrate [Nitrosomonas sp. or Nitrobacter sp. are often employed in the “nitro-denitro” ponds and their residual presence in the effluent could have contributed to increasing the nitrate content in the sample [Parallel to the assimilation of N by microalgae, another explanation for the remarkable N-NH nitrate ,36. UWW e sample ,80. Conce sample .3− (not shown). In UWW alone, an increase in nitrates higher than in UWW with algae occurred, thus making it possible to propose that nitrates produced by nitrifying bacteria activity could have been used by algae, together with ammonium, to support growth. This further opportunity to make use of N by algae, however, did not benefit algae in the synthetic medium, where N-NH4+ could not be converted into N-NO3−, thus further explaining the strong difference in growth between algae in modified BG11 and in the UWW.Overall, our results can support the hypothesis that the conversion of ammonium into nitrate, together with the availability of carbon derived from bacteria activity as reported above, could have contributed to promoting algal growth in the UWW, but not in the synthetic medium, without bacteria. In a preliminary test, in fact, the employment of the UWW alone or inoculated with algae resulted in a different increase in N-NO750 of 0.050, and contained a solid clear residue that, after freeze-storing, tended to precipitate on the bottom of the bottle (The substrate employed for the present research was secondarily treated UWW effluent from the HERA SpA (Holding Energia Risorse Ambiente) WW-TP located in Ferrara, Italy . In January 2019, wastewater was collected from the “sludge line” of the domestic wastewater treatment process, after the thickening step. Part of the UWW effluent was immediately used for the isolation of autochthonous microalgae and the remaining part was stored at −20°C for subsequent experiments on N and P abatement by the isolated microalgae. Low-temperature storage of the UWW was intended to prevent possible alterations due to bacterial proliferation that could occur during the long-lasting isolation step see . The free bottle . Before The composition of the UWW, determined following standard certified methods used for water quality analyses at the HERA internal laboratories of analysis, is reported in photons m−2 s−1), at room temperature , for at least 4–6 weeks. During the incubation, microalgal material stratified in two different portions of the jars, on the top and on the bottom (Aliquots of fresh UWW effluent were placed into closed glass jars (150–200 mL volume) and exposed to indirect natural sunlight .Both algae harvested from the upper layer and those isolated from the lower layer through serial plating were preliminarily tested for growth using, besides modified BG11, other cultivation media commonly employed for the cultivation of microalgae, Bristol GR+ and MA medium ,84. OnlyChlorella-like alga) was chosen for further experiments on growth ability and nutrient removal. Experiments were conducted in batch using 500 mL sterilized borosilicate flasks (300 mL culture volume). Algae were inoculated in UWW effluent (treated) or in sterile modified synthetic BG11 medium (control) to obtain a final cell density of around 2 × 106 cells mL−1; cultures were maintained in the environmental conditions reported in the previous paragraph and were manually shaken every day. No additional CO2 was used [After isolation of microalgal strains, the most abundant and growing algal form  and prepared as follows: for fixation and post-fixation steps, glutaraldehyde  and OsO4  were used, respectively. Then, cells were dehydrated in acetone series and embedded in Araldite resin. Ultrathin sections were finally stained with lead citrate and uranyl acetate [Isolates and harvested microalgae were observed with a Zeiss, model Axiophot, photo-microscope under conventional light. Pictures were taken with a Canon PowerShot S40 digital camera (4 megapixels), mounted on the ocular through a Leica D150 system. Ultrastructure of cells was also investigated using a Zeiss EM910 . For TEM observations, cells were harvested through centrifugation .At each experimental time, cell densities of cultures were evaluated with a Thoma’s counting chamber . Cell densities were plotted on a logarithmic scale to obtain the growth kinetics, and the growth rates were calculated according to Giovanardi and co-workers  using thDW L−1) and the daily productivity (gDW L−1 d−1).For dry biomass, aliquots of samples were filtered through pre-dried and pre-weighed glass-fiber filters . Filters with cells were rinsed with 20 mL of distilled water, dried for 72 h at 60°C and weighted until they reached a constant weight . The dryg, 10 min) for pH measurements. A Jenway mod. 3510  bench pH-meter was employed. pH is, in fact, a useful parameter to monitor algal growth in culture media and is an important regulator for nutrient bio-availability [At each experimental time, modified BG11 medium and UWW were harvested by centrifugation , ammonium (N-NH4+) and phosphate (P-PO43−) were quantified colorimetrically using a flow-injection autoanalyzer  [0 and C1 are the nutrient concentration (ppm) at time t0 or t1, respectively [For phytoremediation tests, aliquots of culture media, i.e., synthetic modified BG11 and UWW, were harvested by centrifugation  for N and P quantification. In detail, nitrate  . The remectively .a), 653 nm (Chl b), 470 nm (Cars) and 750 nm (background disturbance). Pigment concentrations were quantified as reported in Wellburn [pigment 10−6 cells.Cell samples were harvested by centrifugation , and the extraction of photosynthetic pigments was performed with absolute methanol according to . The extWellburn  and exprV/FM ratio) of microalgae was determined using a pulse amplitude modulated fluorometer . Samples were prepared as reported for microalgae in previous studies [0) was determined; the maximum fluorescence (FM) was measured by flashing the samples with a saturating light pulse (0.6 s). The maximum PSII quantum yield was calculated as FV/FM ratio, where FV = FM − F0 [Concomitant to photosynthetic pigment analyses, the maximum quantum yield of PSII  for n number of samples . Asterisks in graphs are used to identify the levels of significance: *, p ≤ 0.05; **, p ≤ 0.01; and ***, p ≤ 0.001.Statistical analyses and graphical representations were routinely performed with Microsoft Office Excel 365. Data were compared using Student’s t-test with significance threshold set at 4+ (the main N form) and of P-PO43− in the UWW effluent used for cultivation of our microalgal isolate are noticeable . National and EU regulations for safe water discharge in natural surface waters with low autodepurative capacity (as is the case of the river which receives waters from the WW-TP of Ferrara) after urban wastewater treatment defines total N and total P concentrations, respectively, below 10 and 1 mg L−1, i.e., ppm [Results on the abatement of N-NH4+ and P-PO43− from UWW could be due to a combination of different modes linked to the treatment of the effluent with the microalgal isolate: 1. microalgal assimilation, 2. interaction with other microorganisms and 3. chemical-physical effects. Integration into the microalgal biomass was the most relevant.The abatement of N-NHMoreover, the algal isolate, while removing nutrients from the UWW effluent, produced a high biomass, rich in biotechnologically valuable compounds, such as Chls, Cars, starch and proteins, thus suggesting its profitable use as food/feed supplements or, even better, as biofertilizers . InsteadOn the other hand, cultivation in BG11-modified medium, which did not significantly influence the algal characteristics, could be proposed for maintaining the microalgae as stock cultures to be employed as inocula in case of loss of microalgae in UWW, a possible event especially if applying microalgal technology to WW-TPs. Related to their origin, the composition of domestic wastewaters is subjected to strong daily and seasonal variations, which could impact negatively on algal growth ,99. From


Paeoniae Radix Alba, the root of the plant Paeonia lactiflora Pall, is a common blood-enriching drug in traditional Chinese medicine. Its effectiveness in the clinical treatment of anaemia is remarkable, but its potential pharmacologic mechanism has not been clarified.Paeoniae Radix Alba in the treatment of iron-deficiency anaemia was preliminarily elucidated through systematic and comprehensive network pharmacology.In this study, the potential pharmacologic mechanism of Paeoniae Radix Alba. The ingredients were predicted to target 77 genes associated with iron-deficiency anaemia. In-depth analyses of these targets revealed that they were mostly associated with energy metabolism, cell proliferation, and stress responses, suggesting that Paeoniae Radix Alba helps alleviate iron-deficiency anaemia by affecting these processes. In addition, we conducted a core target analysis and a cluster analysis of protein-protein interaction (PPI) networks. The results showed that four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway and the AGE-RAGE signalling pathway in diabetic complications, may be major pathways associated with the ameliorative effects of Paeoniae Radix Alba on iron-deficiency anaemia. Moreover, molecular docking verified the credibility of the network for molecular target prediction.Specifically, we obtained 15 candidate active ingredients from among 146 chemical components in Paeoniae Radix Alba and their targets and uncovered the mechanism of action of this drug, providing new insights for advanced research on Paeoniae Radix Alba and other traditional Chinese medicines.Overall, this study predicted the functional ingredients in  Worldwide, 46% of children aged 5 to 14 years and 48% of pregnant women suffer from iron-deficiency anaemia (IDA) . IDA is Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall (family Ranunculaceae), is a TCM with the functions of nourishing blood, astringing Yin, preventing perspiration, regulating menstruation, extinguishing liver wind and relieving pain [In China, traditional Chinese medicine (TCM) is not simply a cultural practice, it also has a history of thousands of years of use for the treatment of various diseases. Under the guidance of the overall concepts and principles of syndrome differentiation and treatment, TCM has achieved satisfactory clinical results for anaemia treatment. ing pain , 12.In recent years, an unconventional novel analytical technique called network pharmacology has been widely used in TCM research –16. CombIn this study, network pharmacology was utilized to analyse the active ingredients, drug targets and key pathways of PRA in the treatment of IDA, as shown in Fig. Information on the chemical composition of PRA was gathered from three databases: the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , the Encyclopedia of Traditional Chinese Medicine , and the Bioinformatics Analysis Tool for Molecular mechANisms of TCM  –23. In tHomo sapiens” to identify IDA-related genes.Targets of IDA were identified with four databases: GeneCards , DrugBank , DisGeNET (a platform to explore the relationships between genes and diseases), and the NCBI database  –26. In tP value of − 2 ~ 5, and a rotatable hydrogen bond number (RBN) ≤ 10. If a compound does not violate two or more of the above principles, it can be considered that the compound has soothing properties [Screening of dynamic components can be conducted on the basis of the five rules of Lipinski: a molecular weight (MW) < 500, a hydrogen acceptor number ≤ 10, a hydrogen donor (HDon) number ≤ 5, a log operties . Functiooperties . The OB operties ; a higheoperties , 31. In At present, the methods and techniques of drug target prediction can be divided into four parts according to their principles: (1) ligand prediction based on chemical structure similarity and pharmacophore models; (2) machine intelligence learning and prediction, for which standardized names and clear molecular target correspondence are required; (3) molecular docking, in which receptors are used to make predictions; and (4) combined prediction . BuildinHomo sapiens as the species. To ensure the accuracy of the results, we used P > 0.5 as the constraint condition for the predicted targets, yielding moderate credibility. After that, we obtained the drug active ingredient targets.First, the TCMSP, BATMAN-TCM, and ETCM were chosen as databases for the chemical components of PRA. The active components were screened with the criteria of an OB > 30% and a DL > 0.18, and the component targets were then rigorously predicted by SwissTargetPrediction  and Stitch  . In addiSecond, to consolidate and standardize the data, we identified the gene names of the predicted target proteins with Universal Protein (UniProt), a comprehensive resource of protein sequences and annotation data. UniProt is a compilation of the UniProt Knowledgebase, the UniProt Reference Cluster, and the UniProt Archive . We restFinally, to identify the universal targets between IDA and PRA, we uploaded the two target networks to the Venny 2.1 online server and obtained 77 common targets , and the “merge” tool was utilized to merge the network. The D-I-T-P-D network was obtained.The LibDock module of Discovery Studio 2016 was used to verify the molecular docking based on the functional components of PRA, and a heat map was constructed from the component-core target docking scores.We collected data on the chemical components of PRA from three databases, namely, the TCMSP, ETCM, and BATMAN-TCM. The numbers of chemical components derived from these three databases were 85, 59, and 35, respectively. After removing duplicates, we obtained 146 chemical components. Through screening of OB values and DL values, 13 qualified chemical components were obtained   that conThe development of a disease is usually associated with multiple genes or proteins, as is the case for IDA. In this study, we identified 1923, 60, 25 and 29 IDA-related genes from the GeneCards, DisGeNET, NCBI and DrugBank databases, respectively. Duplicates were removed, and 1939 related genes were obtained. A total of 77 genes were shared between PRA targets and IDA-related genes  > 2× the average degree for the core targets yielded 12 strategic targets. The results of the GO function and KEGG pathway enrichment analyses were very similar to the enrichment results for the 77 targets Fig. . The topP-values, including pertussis and the TNF signalling pathway -adjusted way Fig. . The detThe PPI network comparison of IDA and PRA revealed 75 core targets. To elucidate the biological functions of these targets, we divided the 75 targets into four clusters and subjected them to GO and KEGG pathway analyses . As illustrated in Fig. .1. As ilThe docking score diagram is given in Fig. IDA is a condition that occurs among children in both developing and developed countries, leading to impaired development, activity intolerance, behavioural changes, irritability, and reduced learning ability; women and elderly individuals are also affected , 46. TheIn this study, we identified 77 common targets between PRA and IDA. These targets were mainly enriched for energy metabolism-, cell proliferation-, and apoptosis-related terms. The main properties of these targets were associated with nucleic acid binding, receptors, and transcription factors (9/77); hydrolases and proteases (8/77); and oxidoreductases 13/77). Many other targets had unknown or other attributes. We reviewed several literature sources and found that most of the components downregulate TNF, IL6, PTGS2, CYP3A4, and CASP3 and upregulate PPARG and CAT, further indicating the reliability of the target prediction, as detailed in Table /77. ManyIn addition, we compared the KEGG and GO results for the 12 core targets, the 75 targets revealed in the cluster analyses, and the common targets, and the results showed an IDA correlation. We believe that the processes of nitric oxide biosynthesis, arachidonic acid metabolism, and Th17 cell differentiation play important roles in IDA –97. EffeSubsequently, we searched for the top four KEGG pathways and identified the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway, and the AGE-RAGE signalling pathway in diabetic complications. We concluded that these pathways are involved in the treatment of anaemia –101. IntIn general, we elucidated the multi-component, multi-target, and multi-pathways characteristics of PRA in the context of IDA treatment through network pharmacology. Four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway, and the AGE-RAGE signalling pathway in diabetic complications, were identified, providing new directions for the development of drugs for IDA.Additional file 1: Table 1. Candidate active ingredients of PRA; Table 2. Common targets between PRA and IDA; Table 3. Correlations between components and targets.Additional file 2: File 2a: Table 1 Known therapeutic targets for IDA; File 2b: Table 2 Targets for PRA.Additional file 3. File for all pictures.Additional file 4. File for partial targets with components docking.


N = 8) and 24 healthy control subjects were enrolled in this study, and intestinal bacterial microbiota was investigated by analyzing V3-V4 regions of 16S rRNA gene sequence. Numerous alterations were observed in the gut microbial community of diabetic individuals. These changes were characterized by a significant lowered abundance of Faecalibacterium, Fusobacterium, Dialister, and Elusimicrobium in the nontherapeutic subgroup compared to the healthy control group. Likewise, correlation analysis showed a substantial decline in gut microbiota richness and diversity with the duration of illness. Furthermore, antidiabetic agents restored to some extent the richness and diversity of gut microbiota and improved the abundance of many beneficial bacteria with a significant increase of Methanobrevibacter in the metformin subcategory compared to the nontherapeutic subgroup. In return, they decreased the abundance of some opportunistic pathogens. The findings of this study have added a novel understanding about the pathogenesis of the disease and the mechanisms underlying antidiabetic therapy, which are of potential interest for therapeutic lines and further studies.Type 2 diabetes is a leading cause of morbidity and a common risk of several disorders. Identifying the microbial ecology changes is essential for disease prediction, therapy, and prevention. Thus, our study is aimed at investigating the intestinal microbiota among healthy and type 2 diabetes individuals and exploring the effect of antidiabetic agents on gut bacterial flora. 24 type 2 diabetes (metformin, glimepiride, and nontherapeutic subgroups;  Gut microbiota are miscellaneous groups of microorganisms that inhabit the gastrointestinal tract (GIT) of both humans and animals. It has coexisted with the body in a symbiotic relationship, with significant metabolic and regulatory functions. The intestinal microbiota are modulated by dietary and cultural habits, age, exercise, ethnicity, and genetics, which are distinctive and extremely variable among individuals , 2.Type 2 diabetes mellitus (T2DM) belongs to a cluster of broadly distributed chronic disorders that result from the disruption of sugar metabolism and homeostasis. It has complicated mechanisms and multiple factors implicated , 4. GlobTaken together, the immunologic, metabolic, and regulatory potential of intestinal microflora explains it is significance in health and diseases. Understanding the microbial ecology will help us to recognize the vital ecosystem and provide the essential information for disease prediction and designing specific strategies to modulate gut microbial flora for preventive and therapeutic purposes. In consequence, to clarify the change in the intestinal flora and it is potential in the pathogenesis of T2DM, many studies are necessary. Thus, our study is aimed at investigating the gut microbiota among healthy and type 2 diabetes individuals and exploring the effect of antidiabetic agents on gut microbial flora.This was a case-control study carried out at Namuzaji Center of National Health Insurance Fund, Kosti, White Nile State, Sudan. Throughout January to March 2019, an assembly of Sudanese healthy and type 2 diabetes individuals were randomly invited and enrolled as study contributors. The research protocol had been approved by the Ethics Review Committee of the Namuzaji Center of National Health Insurance Fund in 2019 and applied the Declaration of Helsinki for the research regarding the human subjects. A researcher invited the participants and explained the protocol in detailed information, and written informed consent obtained from everyone.N = 8): metformin (MET), glimepiride (GLIM), and nontherapeutic (NT) subgroups. The eligible subject in T2DM subgroups was at least ≥one month under the daily dose of MET 500 mg (3 times per day), once a dose of GLIM 2 mg, or NT management policy. The participant's exclusion criteria were aged ≤ 18 years, history of acute or chronic illnesses, antimicrobial use at least before 4 weeks, cancer or autoimmune therapy, previous history of GIT surgery or symptomatic disease, pregnancy and breastfeeding, alcohol consumption, and smoking. People who suffered from GIT parasitosis, abdominal disturbance, diarrhea, dysentery or diabetes complications have also been excluded. The participant's information including gender, age, marital status, residence, education level, occupation, height, and weight was gathered using a constructed questionnaire; and body mass index (BMI) also calculated. All the recruited participants had no apparent indication of illness (excluding T2DM for the study cases) as confirmed by clinical examination, past medical history, and laboratory investigations.The study population consists of healthy control (HC) and T2DM individuals diagnosed according to the strategies of the American Diabetes Association. Participant selection was based on the set of targeted criteria, and the main eligibility criterion was aged > 18 years regardless of gender and ethnic group. Based on the treatment protocol, the diabetic group was further categorized into three clusters , 1 μl of each primer, 1 μl of DNA, and 12 μl of ddH2O. PCR conditions were done with an initial denaturation at 98°C for 1 min; 30 cycles of denaturation at 98°C for 10 s, annealing at 50°C for 30 s, and elongation at 72°C for 30 s; and a final extension at 72°C for 5 minutes. The amplified 16S rRNA genes were purified by GeneJET™ Gel Extraction Kit (Thermo Scientific) and sequencing library constructed using Ion Plus Fragment Library Kit 48 rxns (Thermo Scientific) following the manufacturer's recommendations. Afterward, the constructed library had subjected to Qubit quantification and library testing , and then sequenced using the Ion S5™ XL platform.Bacterial DNA was extracted from 200 mg of every fecal specimen by E.Z.N.A.® Stool DNA Kit  based on company guidelines. NanoDrop 2000  had used to assess the quality (Purity and concentration) of the extracted DNA. Subsequently, all the extracted DNA specimens were stored at −80°C until the next step. For DNA sequencing, V3-V4 regions of 16S rRNA gene sequences were amplified by using specific primers with barcode (341F: 5-CCTAYGGGRBGCASCAG-3 and 806R: 5-GGACTACNNGGGTATCTAAT-3). All PCR reactions were carried out in 30 Quality filtering on the raw reads was performed under specific filtering conditions , and Chit-test, Mann-Whitney U test, one-way ANOVA, and Kruskal-Wallis test assessed the difference in numerical data. Fisher's exact test was involved in categorical data as well. A P value of less than 0.05 was considered significant.Statistical analyses of the study data were conducted using IBM SPSS Statistics for Windows, version 21.0 . Categorical data were presented as number and numerical data as mean. Two independent-samples Alpha diversity indices  were analyzed to see the difference in gut microbiota richness  and diversity (Shannon and Simpson). PCoA, NMDS, and beta diversity were assessed with QIIME software (version 1.9.1) and presented by R software (version 2.15.3). Biomarkers were determined by a LEfSe analysis based on LEfSe software with the default setting of LDA score at 4. Furthermore, the Anosim test and Spearman correlation analysis were also involved in the analysis of difference between groups and the relationship of some variables with relative abundance taxa, respectively.P = 0.024. Notably, there was a significant variations in HbA1c between T2DM subgroups, P = 0.044  were recruited in this study. Between HC and either T2DM individuals or NT subgroup, there was no significant variations in the distribution of the study participants among gender, marital status, residence, education level, and occupation as well as BMI. Likewise, there was no significant difference in FBG and disease duration between T2DM subgroups . The male/female ratio was one in both HC and T2DM groups. The average estimated age was 53.87 in diabetic and 47.12 in HC,  = 0.044 .β-diversity based on unweighted unifrac had shown no significant difference between groups, all of the principal coordinate analyses  and NMDS Plot, have exhibited a distinct separation between T2DM and HC samples  were nonsignificantly different between the T2DM and HC groups; however, the abundance of gut microbiota was higher in T2DM compared to HC as noticed in Chao1, Observed species, and ACE indices. Likewise, based on Shannon index, bacterial diversity was higher in the HC group compared to T2DM subjects . On the  Figures . FurtherP < 0.05 .Comparatively, alpha diversity indices were nonsignificantly decreased in the NT subgroup compared to the HC subjects. Between the diabetic subgroups, all of Observed species, Chao1, and ACE significantly declined in the NT compared to MET and GLIM subgroups. Likewise, based on the Shannon index, the gut microbiome of the NT subgroup also showed a lower microbial diversity .Faecalibacterium and Bifidobacterium were the major abundant genera in the HC and T2DM groups, respectively. At the level of relative abundant phylum and genera, there were numerous variations between the T2DM and HC groups  as well as Holdemanella (Firmicutes) and it is upper classification stages starting from family to class. In contrast, of the major depleted bacterial taxa were the genus Faecalibacterium (Firmicutes) and Succinivibrio (Proteobacteria)  .Methanobrevibacter and Bifidobacterium, Bifidobacterium and Unidentified-Ruminococcaceae, and Subdoligranulum and Bifidobacterium were the major abundant genera in MET, GLIM, and NT subgroups, respectively (P < 0.05) and Elusimicrobia (P < 0.05) and an increase of Actinobacteria phylum (P < 0.05) have been detected in the NT subgroup compared to the HC group. At the genus level, the NT subgroup has also shown a lower abundance of several beneficial bacterial taxa including SCFA producers such as members of Bacteroides, Faecalibacterium (P < 0.05), Dialister (P < 0.05), Fusobacterium (P < 0.05), Roseburia, Elusimicrobium (P < 0.05), and Megasphaera compared to the healthy subjects , Faecalibacterium (P < 0.01), Subdoligranulum, Bifidobacterium, Lactobacillus, Agathobacter, Dialister, Roseburia, Parabacteroides, and Megasphaera with age as well as Blautia, Parabacteroides, and Methanobrevibacter with BMI  among T2DM subjects, in particular, in the individuals of the MET and NT subgroups compared to the HC group, which is in line with Zhang et al. [Globally, diabetes mellitus is a persistent health challenge with inevitable complications , 2. The ividuals –23. Someividuals , 24 thatividuals  found thividuals , Bacteron et al.  and Ahmag et al.  and Tao g et al.  reportedg et al. –23, whicStreptococcus and Enterococcus genera, and restored to some extent the elevated F/B ratio. Formerly, many studies presented similar effects concerning MET [Bifidobacterium and Megasphaera was more in MET subcategory compared to HC group and NT subgroup, respectively. Unlike de la Cuesta-Zuluaga et al. study [Megasphaera was more in HC individuals compared to the MET subgroup. Taken together, these findings indicated that the gut microbiota of MET, GLIM, and NT subgroups were divergent as well as highlighted the relation of antidiabetic agents and gut microbiota, and suggested the protecting effect of antidiabetic agents against some opportunistic pathogens. In diabetic individuals, the weak immune system and low abundance of SCFA producers allow the increase of the opportunistic pathogens, which may later be associated with several complications. Streptococcus and Enterococcus have many members of opportunistic pathogens reported to cause intra- or extraintestinal diseases such as bacterial endocarditis, spontaneous bacterial peritonitis, and urinary tract infections [Interestingly, between the diabetic subgroups, many variations in gut microbial communities have appeared. Antidiabetic drugs not only improved the richness and diversity of gut microbes but also enriched the gut ecosystem with many beneficial microbes such as SCFA and vitamin producers, lowered the abundance of some opportunistic pathogens such as ning MET –28. In lning MET  found thning MET  reportedning MET , we founl. study , Megasphfections –31.Bacteroides was significantly positively correlated with FBG, whereas Fusobacteria showed a tendency for positive correlation, which is opposed to Yamaguchi et al. [Bifidobacterium, Lactobacillus, Unidentified-Lachnospiraceae, and Dorea have displayed a significant negative correlation with HbA1c; and several short-chain fatty acid producer bacteria have also shown a negative correlation with FBG and HbA1c; however, it is not significant. The study results are in line with studies [Faecalibacterium with HbA1c. Previously, Tao et al. [Roseburia with HbA1c that is analogous to the current study result. These findings underscored the link underlying the effects of gut microbiota imbalance in diabetic patients. Dysbiosis not only limited the role of intestinal microbiome but also increased the risk of diabetic complications. Extensive exposure to an unbalanced ecosystem can extremely disturb many pathways involved in insulin signaling and production. Modern studies showed that SCFAs improve blood glucose level and metabolic syndrome [Bifidobacterium and Lactobacillus may extremely affect several aspects of life [Notably, i et al.  and Tao i et al.  study, ri et al. , 2. In t studies , 23 thatsyndrome . Indeed,syndrome , 8. Furtsyndrome –36. Butysyndrome . Thus, t of life –39.Faecalibacterium and Bacteroides with age. In line with this study, Tao et al. [Dialister has a significant negative correlation with age, whereas in disagreement, they [Roseburia was significantly negatively correlated with BMI. Formerly, Salamon et al. [Bacteroides with BMI, respectively, which is similar to our study. Unlike our study, Salamon et al. study [Streptococcus with BMI. Analogous to Sedighi et al. study [Lactobacillus. Whereas, dissimilar to Sedighi et al. study [Fusobacterium was negatively correlated with BMI and Bifidobacterium displays a positive correlation with BMI, but it is not significant, which may be explained by the variation in dietary habits and lifestyle. Collectively, these findings highlighted the relationship of BMI and age with gut microbiota, and suggested the lack of potential roles of some beneficial microbes. In this regard, the link of bacteria with age and BMI requires further verification and investigations.Regarding the relationship underlying the link of age and BMI with gut microbiota, the correlation analysis found that gut microbiota diversity and abundance of several beneficial bacteria were declined with BMI. We also found a significant negative correlation for o et al.  study rent, they  found thn et al.  and Jandn et al.  studies l. study  found a l. study , there wl. study , our stuAlthough other studies are necessary to identify the microbiological alterations interrelated to T2DM, our findings are in the same line with recent studies and added a novel understanding about the pathogenesis of the disease, mechanisms underlying antidiabetic therapy, and the interaction of intestinal microbial flora with FBG, HbA1c, age, and BMI, which are of potential interest for further studies.In summary, our study highlighted the particular alterations of gut microbiota in T2DM. Antidiabetic agents improve the richness and diversity of gut bacterial microbiota, enrich the gut ecosystem with beneficial microbes, and serve as a fighter against some opportunistic pathogens. Further studies should particularly focus on the interactions of gut microbiota with HbA1c, age, and BMI.


The acute-on-chronic liver failure associated with hepatitis B virus (HBV-ACLF) was a type of clinical syndrome with rapid deterioration of liver function. It was characterized by short-term elevated bilirubin, ascites, prolonged clotting time, hepatic encephalopathy, organ failures, and high short-term mortality. It was important to predict and evaluate the disease early. This study intended to comprehensively analyze the prognostic factors of patients with ACLF associated with HBV DNA infection through clinical manifestations and laboratory tests, and to establish a corresponding prediction and evaluation model for further clinical guidance.A total of 220 patients were first diagnosed with HBV-ACLF and admitted to and treated at the Department of Infectious Diseases of the First Affiliated Changhai Hospital of the Second Military Medical University from 2009 to 2018. These patients’ records were collected and divided into two groups: (1) 120 patients who were improved and discharged were classified as good prognosis group and (2) 100 patients who died or underwent liver transplantation were classified as poor prognosis group. By analyzing baseline characteristics and clinical indicators of the two groups, the main potential factors affecting prognosis were identified and the corresponding prognostic evaluation model was established. This model’s advantages and disadvantages were compared with classic prognostic scoring systems.p) = 1/(1 + eZ−) was established, where z = 10.0127 + 0.3687 × NEUT (%) − 0.0082 × PLT + 1.8157 × hepatic encephalopathy. The area under receiver operating characteristic (ROC) curve was 0.89, specificity was 80.83%, and sensitivity was 81%. The newly established prognostic model was compared with other three scoring systems including model for end-stage liver disease (MELD), MELD-Na, and ALBI scores. The results showed that the specificity, sensitivity, and area under the ROC curve of the newly established model were significantly higher than the other three scoring systems.The proportion of ascites and the proportion of hepatic encephalopathy of poor prognosis group were significantly higher than those of good prognosis group. The total bilirubin, creatinine, white blood cell count, and NEU (%) levels of poor prognosis group were significantly higher than those of good prognosis group, and the international normalized ratio, albumin (ALB), alanine aminotransferase, Na, Cl, RBC, and PLT levels of poor prognosis group were significantly lower than those of good prognosis group. A new prediction model LR(p) had better prediction accuracy than the other three scoring models of MELD, MELD-Na, and ALBI and could more accurately assess the prognosis of HBV-ACLF, but in the later stage, it was still necessary to expand the sample size for verification.Hepatic encephalopathy, NEU (%), and PLT levels were independent risk factors for predicting the prognosis of HBV-ACLF. The new prediction model LR( It is cThe morbidity of hepatitis B in the Asia-Pacific region is especially high; therefore, HBV infection becomes the most important cause of ACLF. ACLF progresses rapidly, and the mortality is as high as 50–90% . If earlDue to the complicated occurrence and progression mechanism of AFLC, the disease evolved rapidly, and the lesions could involve multiple organs and tissues. The assessment of a single indicator could not fully and accurately reflect the severity and prognosis of the disease. Therefore, it was necessary to further establish a corresponding multifactor prognosis model through the statistical method to improve the specificity and the sensitivity of the prediction. Currently, many classical models were used to evaluate ACLF, and some models were modified and improved based on classical models, but there were still some shortcomings and deficiencies ,9.The earliest Child-pugh (CTP) score had been used clinically for more than 50 years. The evaluation indicators included serum total bilirubin, albumin level, hepatic encephalopathy, ascites, and prothrombin time (PT). So far, it was still one of the classic models used by the internal medicine department and the surgery department to quantify and assess the liver reserve function of patients with cirrhosis. However, there were certain defects. For example, ascites could be affected by diuretics, and albumin level could be changed by intravenous infusion. In addition, the renal function was not reflected in the model, which caused the CTP score not to fully reflect the severity and prognosis of the disease. Model for end-stage liver disease (MELD) and its derived MELD score were originally used to predict the mortality within 3 months after surgery in patients undergoing transjugular intrahepatic portosystemic shunt, which were subsequently found to be used to determine the prognosis and prioritization of liver transplantation. Compared with the CTP score, MELD was more refined and more accurate in predicting the prognosis of end-stage liver disease. Hence, Biggins et al.  improvedThis study retrospectively analyzed the acute-on-chronic liver failure associated with hepatitis B virus (HBV-ACLF) patients admitted to and treated at the Department of Infectious Diseases of the First Affiliated Changhai Hospital of The Second Military Medical University from 2009 to 2018. Through data mining the baseline information, laboratory indicators, and related factors affecting prognosis of HBV-ACLF patients with different prognosis, we established a predictive model, compared it with other models, and investigated its application value.22.1The medical records of inpatients with first diagnosis in line with the HBV-ACLF criteria at the Department of Infectious Diseases from 2009 to 2018 were retrieved in the clinical data record system of the First Affiliated Changhai Hospital of The Second Military Medical University. The main laboratory test data, changes in the condition and clinical medication during hospitalization, and conditions at discharge were also collected. This study was approved by the Ethics Committee of Changhai Hospital, and all patients signed the informed consent form.2.2Guidelines for the Prevention and Treatment of Chronic Hepatitis B ) + 3.8 × loge (TBil [mg/dL]) + 11.2 × loge (INR) + 6.4 × cause . This model was recommended by the US Organ Distribution Network in 2002 as a standard for liver function assessment and prognosis judgment in patients undergoing liver transplantation surgery, thus enjoying wide application. On this basis, Biggins et al. [R = MELD + 1.59 × (135 − Na [mmol/L]).The MELD score was first proposed by Malinchoc. The scoring model could be used to evaluate the need for transjugular portal-body bypass in patients with cirrhosis. In 2001, Kamath et al. revised the formula: Albumin–bilirubin (ALBI) score was a recently developed new model that combines serum albumin and bilirubin to assess the severity of liver dysfunction. The previous study  showed t2.7The prognostic scoring system included MELD score, MELD-Na score, and ALBI score. MELD score formula : 3.78 × 2.8Guidelines for the Diagnosis and Treatment of Liver Failure [The prognostic criteria of patients in this study were based on  Failure  issued b2.9The data from 220 patients were included in the statistical analysis, including 120 cases in good prognosis group and 100 cases in poor prognosis group.t-test or Mann–Whitney U-test. Categorical variables were summarized using frequency and percentage and compared using Chi-square test or Fisher’s exact test.Continuous variables were tested for normality with the Kolmogorov–Smirnov test. Data are presented as mean ± standard deviation or median (percentile 25 to percentile 75). Categorical variables are expressed as number. When appropriate, continuous variables were compared using Student’s P value of <0.1 (defined “a priori”) were considered relevant and included into the multivariate logistic regression analysis. This second analysis was used to define the factors that were independently associated with poor prognosis. All the analyses were performed using R software. A P value of <0.05 was considered to be statistically significant.For all possible determinants of poor prognosis, univariate logistic regression analysis was performed. All variables with a Receiver operating characteristic (ROC) curves were constructed for variables that significantly predict poor prognosis. Areas under the curve with the 95% confidence interval (CI) were calculated.Logistic regression analysis was performed with R 3.5.1 for macOS . We have complete patient records in our study without any missing values.33.1A total of 302 HBV-ACLF patients hospitalized at the Department of Infectious Diseases of Changhai Hospital from January 1, 2009, to October 2018 were included in the study. According to the inclusion and exclusion criteria, 42 cases were excluded due to complication of other hepatitis, tumor, or other underlying diseases, and a total of 260 cases were included in the study. Of the 260 patients, 40 cases were not included in the statistics due to clinical data loss. The list of related data was deleted. Of the remaining 220 patients, there were 190 males and 30 females. According to the prognosis of patients, they were divided into good prognosis group and poor prognosis group. There were 120 cases in the good prognosis group, accounting for 54.55%, and 100 cases in the poor prognosis group, accounting for 45.45% see .3.2p = 0.1). The age of poor prognosis group was significantly higher than that of recovered group . The incidence of hepatic encephalopathy was also significantly higher than that in recovered group (P < 0.001). There was no statistically significant difference in sex ratio between the two groups. Among the two groups of patients with different prognoses, the TBil, Cr, WBC, NEU, and INR levels of poor prognosis group were significantly higher than those of good prognosis group (P < 0.05). The ALB, ALT, Na, Cl, RBC, and PLT levels of good prognosis group were significantly higher than those of poor prognosis group (P < 0.05). There were no statistically significant differences in AST, AKP, TBA, γ-GT, PA, Glu, or other indicators between the two groups (see The proportion of ascites in poor prognosis group was significantly higher than that in recovered group (oups see .3.4p) = 1/(1 + eZ−), where z = 10.0127 + 0.3687 × NEUT (%) − 0.0082 × PLT + 1.8157 × hepatic encephalopathy. The area under the ROC curve was 0.89, 95% CI: 0.85–0.93, specificity was 80.83%, and sensitivity was 81%.In this study, demographic information, baseline clinical indicators, as well as other potential parameters that might affect prognosis in the two groups were subjected to analysis. Many potential parameters for poor prognosis in HBV-ACLF patients were defined, including advanced age, ascites, hepatic encephalopathy, high TBil, Cr, WBC, NEU, and INR levels and low ALB, ALT, Na, Cl, RBC, and PLT levels. Univariate and multivariate logistic regression analyses were used to determine independent risk factors for poor prognosis see . The ROC4As we all know, HBV-ACLF, a clinically serious syndrome, has numerous occurrence and development factors. The results of this study showed and confirmed some factors affecting its prognosis.The age factor was significantly associated with HBV-ACLF. This study found that the age of poor prognosis group was significantly higher than that of good prognosis group. Therefore, age might be a potential parameter influencing the prognosis of HBV-ACLF. In this study, white blood cell level and neutrophil ratio were positively correlated with the patients’ mortality. The white blood cell and neutrophil levels of poor prognosis group were significantly higher than those of good prognosis group. Elevation of white blood cell and neutrophil levels suggested an aggravation of infection or inflammation. The previous study  suggesteIn terms of liver function, patients with liver failure may develop ascites due to the decreased ability of the liver synthesizing protein, low colloid osmotic pressure, portal hypertension, aldosteronism, increase in antidiuretic hormone, insufficient effective circulating blood volume, and many other factors. In addition, hepatic encephalopathy is a serious complication of end-stage liver disease, which plays a promoting role on the prognosis of ACLF. In this study, the incidence of ascites and hepatic encephalopathy in the poor prognosis group was significantly higher than that of good prognosis; therefore, the occurrence of ascites and hepatic encephalopathy suggested a poor prognosis of ACLF patients.Refractory hyponatremia and persistent damage of the renal function are common clinical manifestations in ACLF patients. Especially in the late stage of ACLF, hyponatremia is irreformable, and the continuous increase in urea nitrogen and creatinine levels is still easy to cause hepatorenal syndrome. In this study, the creatinine level of poor prognosis group was significantly higher than that of good prognosis group, while the blood sodium level was significantly lower than that of good prognosis group. Biggins et al. included serum sodium in the MELD score and improved it, confirming the importance of changes in the blood sodium level in predicting the prognosis of ACLF patients. Therefore, monitoring of blood sodium and serum creatinine levels should be strengthened during clinical diagnosis and treatment.ACLF patients suffer from severe liver damage, massive destruction of liver cells, reduction in liver transaminase synthesis, decrease in blood coagulation factors, and decline in bilirubin metabolism due to various factors, which leads to decrease in the serum transaminase level, elevation of the bilirubin level, and coagulation disorders. The liver function and coagulation function in both groups were significantly abnormal, but the total bilirubin level and INR level of poor prognosis group were significantly higher than those of good prognosis group, while the ALT level was significantly lower than that of good prognosis group.The HBV-ACLF scoring system can help clinicians to predict the early prognosis of the disease, assess the severity of the disease, and provide guidance for the selection of active and effective treatments. However, due to different factors such as genetic polymorphism, race, and disease occurrence and development, each scoring system has a certain degree of defects. This study assessed HBV-ACLF patients with clinically common scoring systems such as MELD, MELD-Na, and ALBI. For example, ascites could be affected by diuretics, and albumin level could be changed by intravenous infusion. In addition, the renal function was not reflected in the model, which led to the CTP score not fully reflecting the severity and prognosis of the disease. Therefore, the CTP score was not included in the study. The results showed that MELD, MELD-Na, and ALBI scores had also good predictive value for ACLF patients.p) = 1/(1 + ez−), where z = 10.0127 + 0.3687 × NEUT (%) − 0.0082 × PLT + 1.8157 × hepatic encephalopathy. The AUC was 0.89 (95% CI: 0.85–0.93) for logistic regression, 0.78 (95% CI: 0.72–0.84) for MELD, 0.79 (95% CI: 0.73–0.86) for MELD Na, and 0.69 for ALBI scores (95% CI: 0.62–0.76). Delong’s test was used to compare the ROC curves. The AUC of our logistic regression was significantly higher than those of MELD (p < 0.01), MELD Na (p < 0.01), and ALBI (p < 0.01).This study determined the related potential factors affecting the prognosis of ACLF and established a new prediction model through univariate and multivariate logistic regression analysis: LR, and PLT level are independent risk factors for predicting the prognosis of HBV-ACLF. Hepatic encephalopathy is also one of the serious complications of ACLF. In addition, as for individual indicators, the neutrophil ratio and platelet level are independent predictive factors for the prognosis of HBV-ACLF and both have a high predictive value. The neutrophil ratio indicates the severity of the infection or inflammatory response, while platelets reflect the coagulation function of the patients. The aforementioned aspects are of great significance in the occurrence, development, and clinical diagnosis and treatment of ACLF. The number of samples included in this study was derived from a single center, which may have an impact on the accuracy of the scoring system, and multicenter large sample size would be required for further validation of the prediction system in the future. In addition, this study was a retrospective study. All the enrolled patients were Asians of yellow race, so the race was relatively limited.The mechanism of occurrence and development of ACLF is complicated, and it is easily interfered by various factors during the progression of the disease, and the disease can involve in multiple organs, thereby affecting its prognosis. In addition to traditional assessment indicators, many new indicators and new prediction models are emerging, providing clinicians with a variety of lessons. Individual indicators or individual models are still unable to fully assess the prognosis of the disease. In future, to more accurately judge the prognosis of ACLF, on one hand, is necessary to explore new indicators with higher sensitivity and specificity; on the other hand, it is necessary to carry out statistics on large-scale clinical data and to formulate more reasonable prediction models.