E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in  A global analysis of antimicrobial resistance (AMR) across 27,155 genomes and 69 drugs reveals patterns in AMR gene transfer between species and identifies 142 AMR gene candidates, two of which were tested and confirmed as contributing to AMR. One strategy for managing AMR is the large-scale sequencing of infection isolates3, which has yielded tens of thousands of publicly-available genome sequences for each major bacterial pathogen, frequently paired with resistance metadata4.Antimicrobial resistance (AMR) remains a persistent problem in the treatment of bacterial infections. With resistance having been observed against nearly all major antibiotics6. Accurate AMR phenotype prediction models trained on thousands of genomes have been developed for many pathogens such as Escherichia coli8, Klebsiella pneumoniae9, Mycobacterium tuberculosis10, Salmonella enterica11, or multiple species15, with numerous others developed from smaller datasets. However, many of these studies report a significant fraction of their models’ predictive genetic features to have no relationship to known AMR mechanisms. This disconnect between statistically identified and mechanistically established genetic determinants of AMR highlights the current gap in knowledge in AMR genetics and remains a challenge for the real world adoption of ML-based systems for rapidly predicting AMR and informing treatment strategies17.This wealth of data has enabled global analyses on the genetics of AMR, many of which employ machine learning (ML) to predict AMR phenotypes directly from genetic variations18 and GEMMA19, with some specializing in microbial GWAS by rigorously addressing population structure such as DBGWAS20 and Pyseer21. However, the predictive features identified when training ML models to predict AMR naturally provide another source of AMR gene candidates distinct from GWAS. Such analyses can leverage the extensive body of ML literature and tools to draw from a broader range of statistical and algorithmic frameworks than those previously used for GWAS, and as such, there is a growing effort towards developing ML workflows specifically aimed at mechanism discovery within AMR genetics22 and beyond23. Recent ML-aided GWAS have identified genetic and metabolic mechanisms behind resistance in M. tuberculosis24, and demonstrated ML’s competitiveness compared to typical statistical testing at recovering known AMR genes for multiple pathogens25. These successes demonstrate the potential for ML to carry out the role of GWAS for the ever-growing public collection of bacterial genome sequences and AMR data.Data-centric efforts to close this gap have focused on genome-wide association studies (GWAS), yielding tools for conducting the statistical tests underlying GWAS such as PLINK26, 2) systematic annotation of known AMR genes among those features with RGI27, and 3) training of support vector machine (SVM) ensembles to learn relationships between all genetic features and a given AMR phenotype25. This workflow was evaluated for both phenotype prediction accuracy and recovery of known AMR genes among predictive features across 12 pathogenic species spanning 127 species-drug combinations and 27,155 genomes. We find that this approach provides both a comprehensive overview of the distribution of known AMR genes and consistently yields ML models that both accurately predict AMR phenotype and outperform contemporary GWAS methods at recovering known AMR genes. Functional analysis of strongly predictive features yielded a set of 142 AMR gene candidates, of which two were experimentally confirmed to impact resistance in E. coli.We present a machine learning analysis tailored towards AMR gene discovery consisting of three components drawn from previous workflows: 1) pangenome construction by sequence clustering to enumerate biologically-interpretable genetic features28 after filtering for assembly quality and availability of AMR data (Supplementary Data 26 with CD-HIT v4.629 (see Methods), yielding six unique genetic feature types: 1) genes (protein sequence clusters), 2) alleles (protein sequence variants), 3) 5’ variants (300 bp ORF-flanking upstream variants), 4) 3’ variants (300 bp ORF-flanking downstream variants), 5) noncoding feature clusters, and 6) noncoding feature variants  or fewer genomes available in total  .Experimentally derived susceptible-intermediate-resistant (SIR) phenotypes for the genomes were assembled from directly reported SIRs and SIRs inferred from minimum inhibitory concentrations (MICs). MIC breakpoints for SIR inference were determined from genomes with both SIR and MIC values for each species-drug-standard combination  to yield internally-consistent SIR data (see Methods). From 169,693 MICs, 22,772 SIR inferences across 93 species-drug cases were generated for genomes without SIR data. In total, 176,911 SIR phenotypes were assembled across 69 drugs, with 88.3% phenotypes from directly reported SIRs and 11.7% inferred from MICs , with the greatest number of AMR genes identified for major drug classes such as beta-lactams, aminoglycosides, and quinolones.Known AMR genes were identified in each pangenome through direct annotation of alleles by RGI v5.2.0 with CARD ontology v3.1.330 , with log2 odds ratios (LORs) for plasmid over chromosomal genes of 3.0 and 1.8, and LORs for multispecies over single species of 1.5 and 1.3, respectively. Other categories enriched in plasmid but not multispecies genes include chloramphenicol acetyltransferases, ribosomal protection proteins, rRNA methyltransferases, and beta-lactamases   and multispecies (over single species) AMR genes Fig. . Dihydronts Fig. . As rpoBblaTEMs), ribosomal protection proteins tetM, tetO, and tet(W/N/W), 23S rRNA methyltransferase ermB, aminoglycoside 3’-phosphotransferase aph(3’)-IIIa, and lincosamide nucleotidyltransferase lnuG. The blaTEMs were observed exclusively on plasmids, while all other multi-class AMR genes were observed on both plasmid and chromosomal DNA. Given the prevalence of blaTEMs, we conducted a case study on the distribution of complete blaTEM alleles across all species. One variant, TEM-116, was found in gram-positive strains (11 Staphylococcus aureus strains), predicted to be on a plasmid shared with Salmonella enterica strains, and found among S. aureus strains most strongly resistant to cefoxitin . Four hyperparameters (HPs), parameters not learned from the data but fixed in advance to control the learning process and model complexity, were varied to evaluate their impact on model performance. Ensembles under various HP combinations were evaluated over 5-fold cross validation (5CV) for 1) accuracy, as the Matthews correlation coefficient (MCC) on test set genomes to account for class imbalance, and 2) biological relevance, through a “GWAS score” defined as a weighted sum of the rankings of known AMR genetic features after sorting model features by feature weight absolute value. A feature was labeled as a known AMR genetic feature if its corresponding gene cluster was a known AMR gene for the drug of interest as annotated by RGI and PATRIC.To identify AMR-associated genetic features, a ML framework was developed to train models for both accuracy at predicting AMR phenotypes and biological relevance, i.e. ability to assign high feature weights to known AMR genes Fig. . For a gHP optimization was first carried out on a set of 10 species-drug test cases, testing 256 HP combinations to eliminate consistently suboptimal HP combinations before scaling to other species-drug cases   achieved MCC > 0.9 and 78 61%) achieved MCC > 0.8 on the test set during 5CV, and 103 models (81%) recovered at least one known AMR genetic feature among the top 20 features , the SVM ensemble approach broadly outperformed both Pyseer and Fisher’s exact test. SVM ensembles identified more known AMR-associated features than Pyseer in 73 cases (57%), the same number in 38 cases (30%), and fewer features in just 16 cases (13%)  Fig. . Examini3%) Fig. . This re73 cases 7%, the sarlR for ciprofloxacin resistance in S. aureus) could not be explained by these previous failure modes.An examination of the 12 genes recovered by Fisher’s exact test but not by SVM ensemble suggests several possible failure modes of the SVM approach  typically associated with resistance to quaternary ammonium compounds32, were linked to resistance against aminoglycosides, beta-lactams, diaminopyrimidines, and sulfonamides across four species, consistent with previous studies that find SMR transporters associated with resistance against a broad range of antibiotics beyond antiseptics34. Additionally, three different formate dehydrogenase genes  were associated with beta-lactam resistance in Klebsiella pneumoniae, suggesting the importance of formate metabolism in AMR, possibly with respect to stress response35. Finally, a majority of the sequence-variant level candidates (16/27), especially those related to flanking regions (10/13), were the most common variant of their respective gene clusters, suggesting that most observed perturbations to these genes may be deleterious with respect to AMR.43 candidates were functionally well-characterized and consisted of four feature types: 16 genes  allele associated with quinolone resistance, and fumarate reductase subunit D (frdD) allele with a V111D substitution associated with beta-lactam resistance . E. coli BW25113 was chosen as the base strain for validation to make use of the Keio knockout collection36, which is genetically identical to K12 MG1655 across all positions within 40 kb of frdD and cycA based on reference genomes NZ_CP009273.1 and U00096.3. Wildtype (WT) cycA and frdD were defined as the most common allele of the respective genes observed across all E. coli genomes in this study, which were also the alleles present in the BW25113 and K-12 MG1655 reference genomes.We selected two allele candidates related to cycA allele, the fifth highest weighted feature in the E. coli AMR model for levofloxacin, had the highest LOR for resistance among all cycA alleles in 2/4 quinolone drugs  and corresponding ΔcycA mutant  under 60 conditions based on three variables: concentration of ciprofloxacin (CIP), supplementation with known substrates of the D-serine/D-alanine/glycine transporter encoded by cycA37, and choice of rich vs. minimal media (cation-adjusted Mueller–Hinton Broth CA-MHB vs. M9 media with glucose) , three of which involved D-serine and M9 media  in 7/14 beta-lactam drugs with AMR data . Appropriately, an ampC 5’ variant containing the equivalent mutation was ranked 5th in the model for amoxicillin-clavulanate, though no other 5’ variants with the mutation were in the top 50 hits of any other beta-lactam model. To assess whether frdD V111D is simply a byproduct of an ampC promoter mutation or contributes separately to beta-lactam resistance, we examined two frdD mutations both resulting in the V111D substitution but with different effects on ampC transcription as predicted by Promoter Calculator45: 1) 332 T > A, predicted to increase ampC transcription 2.6-fold, or 2) 332TC > AT, predicted to have minimal effect on ampC  generated in either BW25113 or corresponding ΔampC mutant (from the Keio collection36). Maximum cell density achieved by these strains were measured under increasing concentrations of ampicillin in either rich (CA-MHB) or minimal (M9 + glucose) media . These results suggest that frdD mutations yielding the V111D substitution requires ampC to confer beta-lactam resistance and is unlikely to contribute to resistance through any ampC-independent mechanism.Across all strains, only the mutant with both g/L Fig. . The mutE. coli. Many of these results depend on the reliable recovery of rare biological features and events and were enabled by the scale of this analysis, operating on the largest internally-consistent AMR phenotype dataset known to the authors at the time of publication.The exponential growth of publicly-available bacterial genome sequences and resistance metadata provides valuable opportunities for applying statistical methods to elucidate the genetics of AMR at a global scale. By applying workflows in pangenome construction, systematic AMR gene annotation, and machine learning to 27,155 genomes, 12 species, and 176,911 SIR phenotypes, we have characterized the current interspecies distribution of known AMR genes and demonstrated the broad capability of ML to carry out GWAS analyses of AMR, surpassing contemporary statistical methods at recovering known AMR genes. From the most accurate ML models, we have identified 142 AMR genetic feature candidates, two of which we have experimentally verified to impact resistance in 46 and cross-genera47 transfer of AMR genes in clinical environments. However, we also find that AMR gene transfer is much rarer between species differing at higher phylogenetic ranks, with just 8 AMR genes observed in at least 10 genomes outside their main phylogenetic class. It has been suggested that transfer of AMR genes between unrelated species such as between gram-positive (GP) and gram-negative (GN) species is rare but possible, having been inferred for tetracycline resistance proteins, ermB, aph(3’)48, and observed for some beta-lactamases49.First, an analysis of 6332 known AMR genes revealed 925 (14.6%) genes to be present in multiple species, which tended to be plasmid-encoded over chromosomally-encoded in a function-dependent manner. This result is consistent with previous studies finding plasmids frequently responsible for cross-speciesblaTEM revealed one variant, TEM-116, to be present in the GP species S. aureus and found among S. aureus strains with the highest levels of resistance to beta-lactams. TEM-116 was observed on three plasmids of which plasmid NZ_AJ437107.1 was observed in both GP and GN strains, suggesting a potential route of transfer. While blaTEM was only recently reported in S. aureus50, the S. aureus genomes we identified to harbor TEM-116 were isolated as early as 2009, suggesting that this transfer may be a much older phenomenon. Further analysis of isolation dates may enable the reconstruction of timelines for the spread of multispecies AMR genes, and help identify whether specific species act as reservoirs for enabling interspecies AMR gene transfer. Given these observations, the transfer of AMR genes between GP and GN strains should be treated as a potential significant contributor to the spread of resistance, and large-scale analyses will likely be necessary to continue capturing these rare events of AMR gene transfer between unrelated strains.A case study of 51. Additional analyses are needed to explore the practical limits of HP optimization for SVMs and AMR genetics, especially regarding how the competing metrics of accuracy and biological relevance may limit performance gains.We then developed a ML workflow for identifying AMR-associated genetic features by optimizing models for both phenotype prediction accuracy and biological relevance, with the latter quantified through a score based on the rankings of known AMR genes among a model’s predictive features. Among cases with over 1000 SIRs and many known AMR genes, the GWAS score was found to generalize well to unseen known AMR genes, based on experiments randomly hiding known AMR genes from GWAS score calculation. Next, in a systematic analysis of four HPs, we found that the optimal ensemble size rarely exceeded 50 estimators, much lower than previous works and suggesting that smaller, more computationally efficient models are sufficient at this scale. Feature subsampling was also confirmed to improve the recovery of known AMR genes without compromising accuracy in a majority of cases. However, HP optimization offered only modest improvements over using fixed HPs, in contrast to a previous GWAS analysis with neural networks on datasets of comparable scale which found HP optimization to significantly improve performance25. This is likely due to strong population structure resulting from the clonal nature of bacteria, which can lead to significant correlations between causal and hitchhiker mutations that are difficult to distinguish using statistical approaches52. Though the SVM approach presented here does not directly address population structure, it was previously shown that attempting to do so through weighted sampling of strain subtypes did not improve AMR gene recovery over random sampling25. While other tools with different approaches for addressing population stratification may outperform those tested here, ML approaches can supplement AMR gene recovery without requiring the often computationally expensive task of defining population structure in advance. These results suggest that even small SVM ensembles with limited HP tuning can reliably carry out GWAS analyses for AMR genetics.Applying this approach to 127 species-drug cases yielded models that were both accurate and reliably recovered known AMR genes. Compared with Pyseer and Fisher’s exact test, SVM ensembles recovered nearly twice as many known AMR genes in total and near supersets of what could be recovered by the other methods. AMR genes recovered by multiple methods were concentrated among the top 3 features of the corresponding SVM ensemble, suggesting that only genes with the strongest statistical signals are reliably recovered by Pyseer or Fisher’s exact test. Additionally, these results confirmed at a larger scale that accuracy is necessary but does not guarantee biological relevancecycA KO vs. WT in E. coli in various environments, we found the cycA KO confers modest but significant, conditional resistance against CIP, specifically in minimal media supplemented with the CycA substrate D-serine. One possible explanation of this result involves the 1) toxicity of D-serine38, which may be mitigated by reducing uptake through loss of CycA or competitive inhibition by other CycA substrates in rich media37, and 2) the SOS response, which is triggered to differing extents by both D-serine39 and fluoroquinolones40 and may result in an SOS response induction no longer optimal to either stress and ultimately greater CIP susceptibility41. As neither CycA nor D-serine directly influence CIP’s mechanism of action yet ultimately impact growth under CIP exposure, these results provide an example of separate environmental stresses and related genes measurably influencing AMR. Further investigation into D-serine may continue to shed light onto clinically relevant conditional resistance, as D-serine has also been shown to sensitize S. aureus to various beta-lactams53 and concentrations of D-serine similar to those tested here may be encountered in various host environments54. CycA substrate glycine has also been shown to sensitize serum-resistant E. coli but not ΔcycA mutants55, suggesting that CycA plays a role in multiple cases of environment-dependent resistance.Analysis of the most accurate models yielded 142 AMR gene candidates, two of which were selected for experimental validation: amino acid transporter CycA vs. quinolone resistance, and fumarate reductase subunit FrdD vs. beta-lactam resistance. Testing the effects of frdD V111D substitution on beta-lactam resistance revealed that resistance was conferred only in the presence of beta-lactamase encoding ampC, and comparing synonymous codons found this was likely due to overexpression of ampC as its promoter overlaps with the frdD open reading frame42. This substitution was previously associated with ampicillin resistance in E. coli induction experiments but not discussed in relation to ampC56, while a similar synonymous mutation yielding V117V was previously shown to be enriched in amoxicillin resistant E. coli and attributed to ampC overexpression57. Given the prevalence of overlapping genes in bacterial genomes58, future GWAS analyses that identify candidate genes associated with a phenotype will benefit from incorporating the genetic context of such genes into their analysis.Similar experiments on the effect of the Overall, combining pangenomics, systematic gene annotation, and ML provides a workflow for efficiently uncovering patterns of known and candidate AMR genes at the scale of 10,000 s of genomes with potentially greater reliability than contemporary statistical methods such as Pyseer or Fisher’s exact test. The flexibility of ML provides numerous opportunities to continue improving this workflow, such as the direct integration of the known AMR gene recovery into the loss function, use of different model architectures beyond SVMs, input of additional genetic feature types such as SNPs, or benchmarking against other phenotypes beyond AMR. As the number of genome sequences continues to grow, periodic updates to this analysis with improved techniques will likely be able to capitalize on the benefits of scale and steadily deepen our understanding of AMR across the phylogenetic tree. Continued development is necessary to bring ML into the current GWAS toolbox when mining sequencing data for previously unknown genetic determinants underlying complex phenotypes.26: 1) genome status is “WGS” or “Complete”, 2) number of contigs is within 2.5 times the median number of contigs across all assemblies for that species, 3) number of annotated CDSs is within 3 standard deviations of the mean, and 4) total genome length is within 3 standard deviations of the mean. Genomes were further filtered for those having EvalCon fine consistency59 of at least 87%60. Drugs with at least 100 experimental antimicrobial (AMR) measurements were identified per species, and genomes were filtered to those with data for at least one of those drugs. Selected PATRIC Genome IDs are available in Supplementary Data https://github.com/tseemann/mlst), and BioProject IDs were taken directly from metadata available on PATRIC, with distributions available in Supplementary Data An initial set of genomes was taken from the PATRIC database RELEASE_NOTES  and filtered by taxon ID to 12 species among the ESKAPEE pathogens or WHO global priority pathogens released in 2017. For each species, genomes were filtered to those meeting four criteria26. All unique protein sequences across all genome assemblies (as annotated by PATRIC) were clustered using CD-HIT v4.6 with minimum identity 80% and minimum alignment length 80%29. Each cluster was treated as a gene, cluster members as alleles, and the 300 bp upstream of the start codon of each occurrence of the gene as 5’ variants . 5’/3’ variants of a gene were identified by locating all occurrences of all alleles across all genome assemblies and extracting the 300 bp directly flanking those occurrences. Cases in which a 300 bp flanking region is interrupted by a contig break were ignored . A similar clustering analysis was conducted for non-coding features  to identify non-coding feature clusters and nucleotide sequence variants, using CD-HIT-EST v4.6 with the same parameters29. The species-wide genetic variation covered by these six feature types was represented as a binary matrix based on presence/absence calls of each feature for each genome.For each species, genes (protein sequence clusters), alleles (protein sequence variants), and ORF-flanking sequence variants were identified using a sequencing clustering approachSpecies-drug cases with at least 100 experimental susceptible-intermediate-resistant (SIR) phenotypes or minimum inhibitory concentration (MIC) measurements among selected genomes were identified. For each pair, the most common SIR testing standard  was identified from either the metadata or manual curation of BioProject accession IDs  derived from one of the following laboratory typing methods: agar_dilution, agar_dilution_or_etest, bd_phoenix, bd_phoenix_and_etest, broth_microdilution, etest, mic, mic broth microdilution, liofilchem, sensititre, vitek_2. For each species-drug case, MIC-SIR mappings were generated for all MIC-SIR value pairs reported in at least three genomes under the primary standard. Ambiguous mappings  and inconsistent sets of mappings  were removed. A MIC-to-SIR inference scheme was developed as follows:MICs used and MIC-SIR mappings are available in Supplementary Data 27. To link AMR genes identified by RGI to specific drugs, a directed graph was constructed from the CARD ontology using ARO accession IDs as nodes and adding directed edge “U ->V” whenever:U corresponds to a gene and U has the relationship “is_a” to V.U corresponds to a drug and V has the relationship “is_a” to U.U has the relationship “part_of”, “regulates”, “confers_resistance_to_antibiotic”, or “confers_resistance_to_drug_class” to V.V has the relationship “has_part” to U.All protein sequences for each species were annotated using RGI v5.2.0 with CARD ontology v3.1.3A gene was labeled as conferring resistance to a drug if there exists a path from the gene’s node to the drug’s node in this graph. 23S rRNAs, 16S rRNAs, and 50S rRNAs were manually identified from PATRIC text annotations of noncoding features and were similarly linked to specific drugs using the graph, starting from nodes ARO:3000336, ARO:3003211, and ARO:3005003, respectively.Additional drug-specific AMR genes were identified from PATRIC text annotations. Sequences with annotations containing a drug name or identical to the annotation of an RGI-identified AMR gene were identified and manually curated for probable known AMR genes . The overall location of a gene or allele was assigned as 1) “chromosome” if >90% of instances were chromosomal, 2) “plasmid” if >90% of instances were plasmid, 3) “chromosome-leaning” if >50% of instances were chromosomal, 4) “plasmid-leaning” if >50% of instances were plasmid, and 5) “ambiguous” otherwise  were identified by filtering all AMR alleles for mention of “TEM” in the RGI or PATRIC annotation, and for length at least 272aa (95% length of TEM-1). Mutations were called from pairwise global alignment of each allele to TEM-1 using the Biopython Align module63 with scores match = 1, mismatch = −3, open_gap_score = −5, and extend_gap_score = −2. N/C-terminal deletions were ignored. Known TEM variants were identified based on exact matches in the CARD database. Presence of specific blaTEM plasmids in individual genomes was determined by filtering the previous MASH results against PLSDB for mappings with MASH distance <0.025  weighted by class frequency  to address class imbalance issues and L1 regularization  to enforce sparsity in feature selection.For each species-drug case, SVM ensembles were trained to classify genomes as susceptible or non-susceptible  based on the species’ genetic feature presence/absence matrix. To accelerate training, feature count was reduced in three stages: 1) features present or missing in less than 3 genomes were removed, 2) perfectly correlated features were merged, and 3) remaining features were sorted by log odds ratio (LOR) for resistant genomes, and features with the 25,000 highest and 25,000 lowest LORs were retained  on the test set. Biological relevance was scored using the following equation:Hyperparameter (HP) ranges for SVM ensembles were first evaluated on 10 test species-drug cases , and features were sorted by p < 2.5*10−5 and p < 3.6*10−5, respectively). To evaluate co-occurrence with known AMR genes, AMR features found in predominantly in resistant strains were identified for each drug, defined as those occurring in at least 5 genomes of which at least 90% are resistant. Each candidate feature-drug pair was assigned a score based on the sum of 1) number of drugs with significant association based on SIR data, 2) number of drugs with significant association based on MIC, and 3) number of drugs for which at least one resistant genome with the feature does not also have any AMR features found predominantly in resistant strains. Candidate scores are available in Supplementary Data The models for each of the 127 species-drug cases were filtered down to those achieving mean test MCC > 0.8 during 5-fold cross validation. From each remaining model, the top 10 features by feature weight absolute value were identified, and filtered for those that 1) were not already known AMR genes, 2) occurred in at least 10 genomes with SIR data for the corresponding drug, and 3) had positive feature weight and LOR for resistance. Remaining feature-drug pairs were tested for whether the feature was significantly associated with resistance, applying Fisher’s exact test for SIRs and Brunner-Munzel tests for MICs. Tests were applied for the specific drug and drugs of the same class for which at least 5 SIRs (for Fisher’s exact) or 5 MICs (for Brunner-Munzel) were available, and significance was determined at FWER < 0.05 with Bonferroni correction , or known to be associated with a specific AMR mechanism for an unrelated drug class were their own categories, and the remaining genes were categorized as well-characterized candidates. For perfectly correlated features, coding features were selected over noncoding features. For variant-level features, mutations were determined against the most common variant of the parent gene cluster using the Biopython Align module63. Interpretation of E. coli candidates was conducted using reference genomes U00096.3 [https://www.ncbi.nlm.nih.gov/nuccore/U00096.3] for K-12 MG1655 and NZ_CP009273.1 [https://www.ncbi.nlm.nih.gov/nuccore/NZ_CP009273.1] for BW25113.The top 10 features by score for each species-drug class pair were identified, yielding 142 candidates which were categorized by function as annotated by PATRIC and additionally by eggNOG-emapper v2.1.6-43E. coli BW25113 knockout mutants ΔcycA and ΔampC were taken from the Keio collection36. The frdD mutations referred to in this study were introduced into both BW25113 and ΔampC using a Cas9-assisted Lambda Red homologous recombination method. Golden gate assembly was first used to construct a plasmid vector harboring both Cas9 and lambda red recombinase genes under the control of an L-arabinose inducible promoter, a single guide RNA sequence, and a donor fragment generated by PCR which contained the desired mutation and around 200 bp flanking both sides of the Cas9 target cut site as directed by the guide RNA. After allowing the transformed cells to recover for 2 h at 30 °C, L-arabinose was added to the media and the cells were allowed to grow for 3–5 h at which time a portion of the culture was plated. Single colonies were screened using ARMS PCR and amplicons spanning the mutation site, generated with primers annealing to the genome upstream and downstream of the sequence of the donor fragment contained in the plasmid, were confirmed with Sanger sequencing. Confirmed isolates were cured of the plasmid by growth at 37 °C. Both of the frdD mutations that were introduced in this study fell within a guide RNA target sequence. Because Cas9 has a tolerance for some single base mismatches in the guide RNA, a second mismatch was engineered into the guide RNA so that the guide RNA had two mismatches with respect to the successfully mutated target sequence and only one tolerated mismatch with respect to the starting strain. In one case, an intermediary strain was first constructed in which all of the codons falling within the guide RNA target sequence were switched to synonymous ones maximizing the base changes. A second round of Cas9-assisted Lambda Red homologous recombination was then used to restore those codons to their original sequences and introduce the desired mutation at the same time.cycA and frdD experiments: 1) Mueller–Hinton Broth  supplemented with 49 mM MgCl2 and 69 mM CaCl2, and 2) M9 minimal medium  supplemented with 2 g/L glucose. For cycA experiments, media were also supplemented with either 10 mM glycine, D-serine, D-alanine, L-alanine, DL-alanine  or nothing, for a total of 12 possible supplemented media.Two media were used for both cycA experiments, and 0.25, 0.5, 1, 2, 4, or 8 mg/L ampicillin for frdD experiments). Plates were incubated in a microplate reader (Tecan Infinite200 PRO) with shaking at 37 °C, and ODs were read every 15 min. Maximum cell density for each condition and replicate was calculated as the maximum OD600 over 12 h after inoculation minus the minimum OD600 observed for the corresponding media without inoculum. Significant differences in cell density between pairs of strains or conditions was determined with Welch t-tests .Cell densities for strain-media-antibiotic combinations were measured in biological triplicates as follows: Fresh culture samples were prepared (OD600 = ~0.05) in each relevant media. Sample solutions were loaded to Costar flat-bottom 96-well plates , with antibiotics added to varying concentrations . Genomes failing any of five assembly quality thresholds were excluded from the analysis (see Methods). No exclusions were made in validation experiments. AMR validation experiments were conducted with biological triplicates and combinatorial condition design, and all attempts at replication were successful. The experiments were not randomized. Experiments were designed to test hypotheses generated by statistical analysis regarding the effect of specific genetic alterations and environments on AMR, and all possible combinations of conditions were tested. This is in contrast to a broader screening experiment for which comprehensive testing would be impossible and randomization would be required. The Investigators were not blinded to allocation during experiments and outcome assessment. Allocating all combinations of Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting SummarySource Data


The asymmetric supercapacitor device with Co‐BTB‐LB as the positive electrode shows a maximum energy density of 150.2 Wh kg−1 at a power density of 1619.2 W kg−1 and good cycling stability with a capacitance retention of 97.1% after 10000 cycles. This represents a state‐of‐the‐art performance reported for asymmetric supercapacitor device using electroactive bottom‐up metal‐complex nanosheet, which will clearly lead to a significant expansion of the applicability of this type of 2D nanomaterials.Inspired by the significant advantages of the bottom‐up synthesis whose structures and functionalities can be customized by the selection of molecular components, a 2D metal‐organic framework (MOF) nanosheet Co‐BTB‐LB has been synthesized by a liquid–liquid interface‐assisted method. The as‐prepared Co‐BTB‐LB is identified by scanning electron microscopy/energy dispersive spectroscopy (SEM/EDX) and X‐ray photoelectron spectroscopy (XPS), and the sheet‐like structure is verified by scanning electron microscopy (SEM), high‐resolution transmission electron microscopy (HRTEM), and atomic force microscopy (AFM). Co‐BTB‐LB electrode exhibits an excellent capacity of 4969.3 F g Bottom‐up 2D metal‐complex nanosheet Co‐BTB‐LB exhibits good electrochemical properties and cycling stability, and the ASC device with Co‐BTB‐LB as the positive electrode also exhibits high energy density and good cycling stability, which represents a state‐of‐the‐art supercapacitor performance for electroactive bottom‐up nanosheet reported. Among the most common energy storage devices , research on supercapacitors has attracted an increasing attention in recent years because of their high capacitance, good stability, fast charge/discharge processes, and low maintenance cost. However, their narrow voltage window and low energy density severely hinder further application. The charge storage capability of supercapacitors mainly depends on the electrode materials, which need to be further developed.With the increase in awareness of environmental protection, topics on renewable energy sources and sustainable energy storage devices are getting more important in the scientific community.1 Suth nanosheets with 2D polymeric structures, such as silicone, germanene, stanene, and phosphorene, have attracted much attention in the fields of electronics, batteries and supercapacitors, which promote the research and development of other 2D nanosheet materials. Park and co‐workers addressed the emerging nanotechnologies that enable extrinsic pseudocapacitance in 2D nanomaterials, the performances of which can be significantly improved by controlling the structure at the microscopic level such as crystalline phase, defects, pores, and size to the macroscopic level such as morphology and hierarchical structure. Inspired by the significant advantages of unique electronic structures and physical properties, 2D nanosheets are very attractive candidates for supercapacitors in the pursuit of achieving good electrochemical performance because their ideal ordered nanopores and interlayer space for ion intercalation/deintercalation are beneficial for preserving the ion diffusion channel and accelerating the electron transfer. Therefore, the construction of new 2D materials for energy storage and conversion becomes one of the key critical strategies for their future applications. For example, Park and co‐workers showed that by coupling the nanosheets of oxidized black phosphorus (oBP) and reduced graphene oxide (rGO), the surface redox pseudocapacitance of 478 F g−1  was achieved, which exceeds the kinetic and stability limitations of previously explored BP. The representative 2D nanosheets mainly include surface 2D metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs), in which 2D MOFs with high specific surface areas are good electrode materials for supercapacitors due to their adjustable morphology, designable porous structure and interlayer spacing. Hydrothermal method and top‐down exfoliation are the typical strategies to obtain 2D MOF materials. For example, Chen and co‐workers have synthesized 2D hierarchical porous cobalt/nickel‐based vanadates thin sheets via a hydrothermal method by direct decomposition of the mixed aqueous solution of NiCl2/CoCl2 and NaVO3, and the sheet‐based electrode showed a remarkable specific capacity of 848.5 C g−1 (specific capacitance of 2617.5 F g−1) at 1 A g−1. Xia and co‐workers prepared the Co‐Ni‐B‐S MOFs by a hydrothermal method followed by the boronization and subsequent sulfurization, which exhibited a high specific capacitance of 1281 F g−1 at 1 A g−1, and outstanding cycling stability of 92.1% retention after 10 000 cycles. Dai and co‐workers fabricated a nanosheet MoS2@carbon, which delivered a specific capacitance of 1302 F g−1 at a current density of 1.0 A g−1 and showed a 90% capacitance retention after 10 000 charging–discharging cycles. Fu and co‐workers synthesized a 2D Co‐catecholate (Co‐CAT) which delivered an exceptionally high capacity of 1160 F g−1 at 1 A g−1 and a special self‐discharge rate (86.8% after 48 h). Huang and co‐workers fabricated 2D mesh‐like vertical structures (NiCo2S4@Ni(OH)2) by hydrothermal method, and the resultant flexible electrode exhibited a high areal capacity of 535.9 µAh cm−2 (246.9 mAh g−1) at 3 mA cm−2 and outstanding rate performance with 84.7% retention at 30 mA cm−2. Hao and co‐workers fabricated ultrafine Ni(OH)2 nanosheets grown on 3D graphene hydrogel by electrochemical exfoliation for supercapacitor applications, which showed a specific capacity of 1603 F g−1. Although the hydrothermal method is an effective way to afford nanosheet materials, this strategy usually requires high temperature, which often leads to uncontrollable growth of nanomaterials with serious agglomeration that has hindered its large‐scale application in supercapacitors. It has been reported that the properties of top‐down nanosheets prepared by exfoliation of bulk‐layered crystalline mother materials are greatly limited by their mother materials. Recently, MOF nanosheets were produced by liquid–liquid (L–L) interfacial synthesis with an aqueous solution of metal ions and an organic solution of ligands. Compared with the hydrothermal method and top‐down exfoliation strategies, L–L interfacial method is a controllable synthesis. A significant advantage of the bottom‐up synthesis of nanosheet is that the structures can be customized through the choice of components . More importantly, the redox‐active organic linkers and variable valence of metal centers enable MOF nanosheets with graphene‐like structures to possess high porosity, high surface area, and a lot of active sites. Very recently, the metal‐carboxylate coordination bond is one of the most important chemical bonds in MOFs reported. The balance between metal‐carboxylate bond strength and its reversibility is useful in forming superstructures with ordered nanopores and layered structures. For example, Kern and co‐workers first used the metal‐carboxylate coordination bond to create a 2D polymer structure at a well‐defined metal surface under ultrahigh vacuum (UHV) conditions. Nevertheless, while pyrrole and pyridine derivatives as excellent N‐donor ligands have been used to construct numerous 2D nanosheets, reports on 2D nanosheets constructed from carboxylate‐based ligands are still very limited.Since the discovery of graphene,6 nano and found that there are scarce reports on the application of bottom‐up metal‐complex nanosheets for supercapacitors. Herein, we report a stable and easily prepared MOF nanosheet Co‐BTB‐LB synthesized from 1,3,5‐tris(4‐carboxyphenyl)benzene (H3BTB) ligand and cobalt(II) nitrate hexahydrate. In view of the fact that superstructure with ordered nanopores and layered structure can provide much more active sites for the as‐synthesized nanosheet Co‐BTB‐LB, it is expected to be readily applied in asymmetric supercapacitor (ASC). As expected, Co‐BTB‐LB exhibits good electrochemical properties of high capacity of 4969.3 F g−1 and discharge time of 2236 s at the current density of 1 A g−1 and good cycling stability with 75% capacity retention after 1000 cycles. Furthermore, the ASC device with Co‐BTB‐LB as the positive electrode shows a maximum energy density of 150.2 Wh kg−1 at a power density of 1619.2 W kg−1 and good cycling stability with a capacitance retention of 97.1% after 10 000 cycles performed at a current density of 10 A g−1, which reveal the potential applications of nanosheet Co‐BTB‐LB in energy‐storage devices.Given this background, we have been working on the synthesis of the optoelectronic bottom‐up nanosheets,27 and22.12.1.12Cl2 solution of the ligand H3BTB  was added into a glass vial with a volume of 50 mL and a diameter of 3.2 cm. Solutions were then covered with degassed D.I. water (10 mL) to form a buffer layer before the addition of 10 mL water solution of Co(NO3)2·6H2O (5.0×10−2 mol L−1). The reaction was allowed to proceed for 15 days at room temperature. After that, nanosheet emerged at the interface as a layered film, which was marked as Co‐BTB‐LB. After the removal of aqueous and organic phases, the as‐prepared nanosheet was washed thoroughly with water, ethanol, and CH2Cl2, and dried in vacuo.Degassed CH2.1.23)2·6H2O was added to a degassed DMF solution of the ligand H3BTB , and the mixture was sonicated for 10 min in an ultrasound system. Then, the mixed solution was shifted into a Teflon‐lined stainless steel autoclave (100 mL capacity) and heated at 170 °C for 12 h. After the mixture was cooled to room temperature naturally, the obtained brown precipitate was thoroughly washed and centrifuged with DMF several times. Finally, the collected sample was placed in a vacuum oven at 60 °C and dried for 24 h. The as‐prepared sample was marked as Co‐BTB‐HT.1.16 g of Co with the ligand unit containing carboxyl group. As shown in Figure2+ and carboxyl group is represented in (I), which was also confirmed by the structures of MOFs in the literature . In order to confirm the type of interaction of Co‐BTB‐LB, a small molecular model M1 has been synthesized as depicted in Scheme 2+ in D.I. water without pH adjustment at room temperature, and the reaction condition of which was the same as that in the synthesis of Co‐BTB‐LB. The product M1 has been characterized by proton nuclear magnetic resonance (1H NMR), fourier‐transform infrared spectoscopy (FT‐IR) and liquid chromatography‐mass spectrometry (LC‐MS). As shown in Figure δ, above 12.84 ppm) compared with that for the ligand 3,5‐diisopropylbenzoic acid as depicted in Figure 3 also have different degrees of shifting between them, indicating that the type of interaction of M1 is represented as (II). LC‐MS was employed to confirm the product by the respective molecular ion peak and fragment as shown in Figure 2+ and H2O molecules resulted in the appearance of strong vibrational bands in the spectral range of 3429 and 798–783 cm−1 for M1 and nanosheet Co‐BTB‐LB. Upon the coordination of H3BTB with Co2+, the position of C=O is shifted to lower wavenumbers for M1 and nanosheet Co‐BTB‐LB, which indicates that the carboxylic oxygen is coordinated by Co2+. In addition, the appearance of Co‐O vibrational bands of M1 and nanosheet Co‐BTB‐LB at 596 and 582 cm−1 also confirms the coordination behavior of H3BTB and Co2+. Besides, it is stable in air and in 1 M KOH aqueous solution 2·6H2O functions as a counter anion for such cobalt complex motif, and the topological structures of the nanosheet Co‐BTB‐LB is illustrated accordingly (NO3− is omitted for clarity).The MOF nanosheet Co‐BTB‐LB has been synthesized between the metal ion Con Figure 28 thee Figure .[29]2.1.4FigureN,N‐dimethylformamide at 170 °C, resulting in a solid material (Co‐BTB‐HT) far from a film texture  in Figureπ‐π conjugated structure in the nanosheet Co‐BTB‐LB. The interplanar spacing of 0.25 nm is attributed to the CoO(111) crystal plane. The as‐synthesized nanosheet Co‐BTB‐LB and bulk Co‐BTB‐HT from the hydrothermal method were examined by powder X‐ray diffraction (PXRD) to investigate the crystallinity and phase purity. As revealed from PXRD, nanosheet Co‐BTB‐LB and bulk Co‐BTB‐HT displayed one main diffraction peak at the 2θ value of 34.15°, which can be attributed to the (111) plane of cubic phase CoO (JCPDS No.42‐1300). The PXRD method was also used to investigate the nanosheet Co‐BTB‐LB after soaking in 1 M KOH for 6 h. As shown in Figure As shown in e Figure . Its disFigureFigure2+ is present in Co‐BTB‐LB. The binding energies of Co 2p3/2 and Co 2p1/2 of Co‐BTB‐LB are at 781.7 and 797.8 eV, which are attributed to the peaks at Co2p3/2 and Co2p1/2 of Co2+, respectively. Notably, two other apparent satellite (sat.) peaks at 786.7 and 803.5 eV are related to the Co2+ arising from the chemical reaction due to the coordination of Co2+ with carboxylic oxygen from the H3BTB ligand. As shown in Figure 2+ and H3BTB ligand. To investigate the narrow XPS spectra focusing on Co for the product Co‐BTB‐HT by hydrothermal method  and XPS were also conducted to analyze the chemical elements of nanosheet Co‐BTB‐LB. The SEM/EDX mapping images of Co‐BTB‐LB reveal the homogeneity in the distribution of C, O and Si on Co‐BTB‐LB as proposed Figure5. Addid Figure , the two2 g−1, respectively. The former one is nearly seven times higher than that of the latter counterpart, which may be attributed to the notion that the mild bottom‐up synthesis method for nanosheet Co‐BTB‐LB is much more favorable to the formation of a superstructure with ordered nanopores and layered structure.In order to investigate their adsorption–desorption behavior, the Brunauer‐Emmett‐Teller (BET) tests were conducted for both nanosheet Co‐BTB‐LB and bulk Co‐BTB‐HT  and (2) as follows .Since the superstructure with ordered nanopores and layered structure can provide much more active sites for the as‐synthesized nanosheet Co‐BTB‐LB, it is expected to be useful in supercapacitor application. To evaluate the electrochemical performance of Co‐BTB‐LB electrode, the electrode with an initial mass loading of 0.15 mg was prepared and the electrochemical measurements were investigated in 1 M KOH electrolyte. The cyclic voltammetry (CV) performance of the Co‐BTB‐LB electrode at different scan rates from 2 to 50 mV sThe area of the CV and its current response also increase significantly as the scan rate increases, which indicates that Co‐BTB‐LB and Co‐BTB‐HT are beneficial to the fast and reversible Faradaic reaction Figure . The cap36−1 with the voltage window of 0‐0.45 V are shown in Figure  The discharge times are 2236, 1090, 408, and 184 s for nanosheet Co‐BTB‐LB under the current density of 1, 2, 5, and 10 A g−1, respectively , Δt is the discharge time (s), im is the current density (A g−1) and ΔV is the potential window (V). As the current density increases, the specific capacitance of nanosheet Co‐BTB‐LB and sample Co‐BTB‐HT will gradually decrease, because when the current density becomes smaller, there is enough time for OH− to transfer between the electrode surface and the solution, which is of benefit for OH− to embed in or away from the material. In other words, more charge can be transferred and stored, so a higher specific capacitance can be achieved. As shown in Table −1 have been achieved for the nanosheet Co‐BTB‐LB electrode at various current densities of 1, 2, 5, and 10 A g−1, respectively. However, at the same current densities as Co‐BTB‐LB, Co‐BTB‐HT electrode exhibits specific capacitance values of 151.9, 118.7, 62.8, and 3.8 F g−1 at various current densities of 1, 2, 5, and 10 A g−1, which are far lower than that of the nanosheet Co‐BTB‐LB electrode. Table − to penetrate into Co‐BTB‐HT and react with the active sites, which is harmful to the adsorption and desorption of OH−, and makes it impossible for electrons to be transmitted on time. As shown in Figure −1 in the potential range of 0 to 0.45 V. The specific capacitance of the test sample can be stabilized at ≈75% of the initial value after 750 cycles until 1000 cycles. To further compare the fundamental capacitive behaviors of the two electrode materials for supercapacitors, the electrochemical impedance spectra (EIS) tests were also performed to analyze the resistance, ionic conductivity, charge, and mass transfer for nanosheet Co‐BTB‐LB and sample Co‐BTB‐HT. The EIS Nyquist plot is generally composed of a semicircle in the high frequency region and a straight line in the low frequency region. The semicircle corresponds to the charge transfer resistance (Rct), the straight line reflects the diffusion resistance of the electrolyte, and a constant phase element (CPE) is used to compensate for the dispersion effect. Electrolyte resistance (Rs) which is generated from the resistance between electrode material and electrolyte reflects the conductive discrepancy of the material. Diffusion can also cause impedance, called Warburg impedance (Wo), which depends on the frequency of the potential disturbance. As shown in Figure Rct = 0.75 Ω) in the high frequency region suggests a fast charge transfer in Co‐BTB‐LB electrode. The inclination angle of the curve is obviously >45° in the low frequency region, showing a more obvious diffusion process in this region. The small electrolyte resistance (Rs = 0.11 Ω) suggests the low resistance between the electrode material and electrolyte during the electrochemical reaction, suggesting the excellent charge transfer performance for nanosheet Co‐BTB‐LB. This is because the interlayer space of Co‐BTB‐LB and the micro/mesopore formed by the stacking of Co‐BTB‐LB nanosheets is conducive to the migration of OH−. Compared to nanosheet Co‐BTB‐LB, the values of Rct and Rs for Co‐BTB‐HT are 0.88 and 0.15 Ω, respectively, which are greater than that of nanosheet Co‐BTB‐LB, indicating a relatively lower charge transfer.In addition, galvanostatic charge‐discharge (GCD) technique was applied to characterize the specific capacitance of the electrodes. The GCD curves of the nanosheet Co‐BTB‐LB and bulk Co‐BTB‐HT at the current density of 1, 2, 5, and 10 A gn Figure 37 The−1  under the same condition, indicating that the relatively low mass loading at 0.15 mg still provides accurate enough specific capacitance of the Co‐BTB‐LB electrode. Furthermore, in order to eliminate the influence of Ni foam on the specific capacitance, a contrast test of blank Ni foam has been made. Under the same test conditions, the discharger time for blank Ni foam is 16.4 s at 1 A g−1 and the specific capacitance value is only 39 F g−1 , P is the power density (W kg−1), sC is the specific capacitance (F g−1), V is the voltage and Δt is the discharge time (s). Figure −1. Based on the CV curves of Co‐BTB‐LB and AC in the three‐electrode system and the ASC device, the ASC device can easily reach a voltage of 1.8 V /GF//Ni‐Co‐S/GF, Co3O4@NF//CNT@HCNF‐1.5, and Fe‐Co‐Ni MOF//AC ,,5), the 41(4Ω Figure , which v] Figure . Further1 Figure . Light‐e1 Figure , demonst3−1 at the current density of 1 A g−1 and good cycling stability with 75% capacity retention after 1000 cycles. The ASC device with Co‐BTB‐LB as the positive electrode shows a maximum energy density of 150.2 Wh kg−1 at a power density of 1619.2 W kg−1 and good cycling stability with a capacitance retention of 98% after 5000 cycles performed at a current density of 10 A g−1, demonstrating the potential applications of nanosheet Co‐BTB‐LB in energy‐storage devices. To our knowledge, this is a state‐of‐the‐art performance reported so far for electroactive bottom‐up metal‐complex nanosheet applied to supercapacitor, which will clearly lead to a significant expansion of the applicability of this type of 2D nanomaterials as functional nanomaterials.A 2D MOF nanosheet Co‐BTB‐LB has been successfully synthesized by a facile liquid–liquid interface‐assisted method. The as‐prepared nanosheet Co‐BTB‐LB has good structural stability and large size domains, which have been identified by SEM/EDX and XPS. The sheet‐like structure of nanosheet Co‐BTB‐LB has been verified by SEM, HRTEM, and AFM, revealing a flat morphology and a domain size with >10 µm in both height and phase images, and the cross‐sectional analysis was conducted to a typical thickness of 88 nm. The nanosheet Co‐BTB‐LB can show a high utilization of the redox‐active sites, good electrochemical stability and energy storage properties, relative to the corresponding powder sample Co‐BTB‐HT by hydrothermal method, which is indicative of the superiority of the MOF nanosheet prepared by the liquid–liquid interface‐assisted approach. Co‐BTB‐LB electrode exhibits good electrochemical properties of high capacity of 4969.3 F gThe authors declare no conflict of interest.Supporting InformationClick here for additional data file.


With 400,000 new cases and over 80,000 deaths a year worldwide, endometrial cancer (EC) holds a rather unfortunate record, namely, that of the tumour with the highest increase in incidence, a unique trend among gynaecological cancers . EC is mTo date, the standard management of early-stage cancer includes surgery, radiotherapy and/or chemotherapy. Thus, there is a clear unmet medical need for disease management, and it is essential to find a molecular target to better classify ECs in terms of prognosis and to drive the therapeutical choice. The intention of this Special Issue is to increase our understanding of EC development, which may lead to the discovery of new molecular diagnostic technologies and targeted therapeutics. For instance, although EC is a hormone-dependent neoplasm, there are no recommendations for the determination of steroid hormone receptors . CurrentMoreover, advancements have newly been made in EC pathological classification through the new molecular classification that allows for a more accurate categorisation of the pathology, which is useful for making a therapeutic choice. In fact, for decades, EC risk stratification has been based on histopathological features, such as tumour grade and histotype, depth of myometrial invasion, and cervical and adnexal involvement. The Cancer Genome Atlas (TGCA) has proposed a new and disruptive molecular classification of ECs which identifies, based on molecular characteristics and expression, four distinct subgroups. This approach has revolutionized diagnostics, allowing data to be refined from a precision medicine point of view through the definition of a real therapeutic algorithm ,8.Furthermore, in recent years, vibrational biospectrometry methods for the screening and diagnosis of EC have gained ground. Vibrational biospectroscopy is a noninvasive objective technique that uses the interaction of light with tissue to obtain detailed information about the chemical composition of biological samples. Current diagnostic strategies are time-consuming, invasive and have limited accuracy, and there is no population-level screening. Treatment depends on the patients’ health, type of disease and prevalence at the time of diagnosis. Most women will be offered surgery; however, the role of lymph node dissection in the early stage of the disease remains controversial. There is, therefore, a need for a test that is objective, accurate, and capable of detecting pre-cancer and early cancer, as well as able to identify metastatic lymph node involvement, so that lymph node excision is performed only when necessary. However, while there has been significant progress made in vibrational spectroscopy development, the techniques are still at the trial phase and have not yet been translated into clinical practice .Moreover, for over 20 years, the availability of a new therapy capable of affecting the prognosis of a tumour which, although not considered particularly aggressive, had a proportion of recurrences that were difficult to treat, had not been verified. In the last year, interesting advances have taken place with the introduction of immunotherapy for the treatment of ECs. The drug dostarlimab is an antibody that blocks the PD-1 receptor, and is indicated for adult patients with advanced or recurrent EC with a mismatch repair/microsatellite instability system defect. This immunotherapy drug, in fact, recently had accelerated approval by the US FDA, and the European Commission itself gave the green light to its conditional marketing. This approval is based on results obtained through the GARNET multi-cohort study ,11. The The main aim of this Special Issue is to provide an open-source sharing platform for significant works in the field of molecular oncology that can increase our understanding of EC development, and which may lead to the discovery of new molecular diagnostic technologies and targeted therapeutics.


Dopamine D1 receptor seems to play a role in mediating plasticity. Therefore, the present study aimed to investigate the effects of passive avoidance tasks postexposed to BPA on dopamine D1 receptor density in the hippocampus, amygdala, and cerebellum of male rats.Thirty‐five male Sprague–Dawley rats weighing 220.300 g, in standard light‐dark 12 h light/12 h dark were used in the present study; water and food were ad libitum. Animals were divided into six groups. Administration of BPA 5 and 50 mg/kg/day were gavaged for 15 days. Learning and memory assessment were done by a shuttle box after 15 days of BPA administration. The density of the dopamine D1 receptor was investigated using an immunohistochemistry (IH) procedure. For determining the color difference in IH sections, Image Analyzer software was used. The data were analyzed by one‐way ANOVA followed by Tukey's as a post hoc test.The data showed that BPA in both doses could significantly increase the density of dopamine D1 receptors in the hippocampus, amygdala, and cerebellum of male rats; learning in rats postexposed to BPA improves dopamine D1 receptor density significantly in three brain structures.According to the results, passive avoidance learning and memory can improve the density of dopamine D1 receptors in the hippocampus, amygdala, and cerebellum of male rats. Bisphenol A is a xenoestrogen that could be destroyed dopaminergic neurons. It has many applications as polycarbonate plastic and epoxy resin production. Our results represent that passive avoidance learning and memory can reduce the toxic effect of bisphenol A on dopamine D1 receptors density in some brain structures. However, they are chemically distinct from the natural estrogen compounds produced by the endocrine glands. An example of these compounds is bisphenol A (BPA), which is used in polycarbonate plastics and epoxy resin production  region, amygdala, and Purkinje cell of the cerebellum in male rats preexposed to BPA.22.1n = 7). (1) NL‐ Sham (which received BPA vehicle without learning); (2) L‐Sham (which received BPA vehicle + learning); (3 and 4) NL‐BPA ; (5 and 6) L‐BPA (which received BPA in two dose + learning). After BPA or BPA vehicle treatment, the passive avoidance procedure was began. The BPA 5, 50 mg/kg/day was used orally for 15 days  and controlled temperature (20 ± 4°C) condition. Animals were randomly allocated to six equal groups  to flush blood out of the brain vasculature, then perfused with 4% formaldehyde. Then, the whole brain was removed and washed with normal saline. Brain fixed for 24 h in 10% formaldehyde in 0.1 M phosphate buffer (pH 7.4). It was followed by post fixing in 4% formaldehyde in 0.1 M phosphate buffer (pH 7.4). Tissue processing was accomplished in an automated tissue processor, and paraffin blocks were prepared. Furthermore, 5 μm serial sections of the amygdala, hippocampus CA1 region, and cerebellum Purkinje cell were made and mounted on 25% L‐lysine coated glass slides.2.4−3) were used . Our immunohistochemistry protocol was according to “RRID: SCR_002396.”The immunohistochemical study was according to reference .Statistical analysis was performed using the SPSS (version 21) software. Factorial ANOVA and One‐way ANOVA were used, and Tukey's as the post hoc test was performed to determine the difference between groups. Significant level was All the procedures involving animal subjects were reviewed and approved by the Institutional Research Ethics Committee of the School of Veterinary Medicine of Shiraz University (code# 94GCU2M1755).3p < .001) in the density of dopamine D1 receptors in the amygdala, hippocampal CA1 region, Purkinje cell of the cerebellum in comparison to the NL‐sham, while the BPA 50 mg/kg/day significantly increased (p < .01) the density of dopamine D1 receptor relative to the BPA 5 mg/kg/day in the amygdala, hippocampal CA1 region, Purkinje cell of the cerebellum  was higher than NL‐sham in the amygdala, hippocampal CA1 region, Purkinje cell of the cerebellum. But the density of dopamine D1 receptor significantly (p < .01) was lower in both L‐BPA than both NL‐BPA in the amygdala, hippocampal CA1 region, Purkinje cell of the cerebellum  subtypes  despite that BPA displays 1000‐ to 2000‐fold less affinity to the ERs than does 17β‐estradiol (E2), the most active estrogen (Acconcia et al., In the present study, BPA as an estrogen partial agonist led to dopamine D1 receptor up‐regulation. More detailed investigations are suggested on the dopamine D1 receptor gene expression. In the present study it was also shown that learning could improve the toxic effects of BPA. This could be attributed to the increase in the brain estrogen production caused by learning and memory processes.Mahnaz Taherianfard: study design, conceptualization, writing‐original draft, writing‐review, data analysis, funding acquisition and editing. Saiedeh Ahmadijokani: methodology and data acquisition.The authors declare no conflicts of interest.The animal handling was conducted according to the Ethical Committee for Animal Experiments at Shiraz University.Animal welfare code: 94GCU2M1755.https://publons.com/publon/10.1002/brb3.2942.The peer review history for this article is available at


Recently MXenes has gained immense attention as a new and exciting class of two-dimensional material. Due to their unique layered microstructure, the presence of various functional groups at the surface, earth abundance, and attractive electrical, optical, and thermal properties, MXenes are considered promising candidates for various applications such as energy, environmental, and biomedical. The ease of dispersibility and metallic conductivity of MXene render them promising candidates for use as fillers in polymer nanocomposites. MXene–polymer nanocomposites simultaneously benefit from the attractive properties of MXenes and the flexibility and facile processability of polymers. However, the potentiality of MXene to modify the electrospun nanofibers has been less studied. Understanding the interactions between polymeric nanofibers and MXenes is important to widen their role in biomedical applications. This review explores diverse methods of MXene synthesis, discusses our current knowledge of the various biological characteristics of MXene, and the synthesis of MXene incorporated polymeric nanofibers and their utilization in biomedical applications. The information discussed in this review serves to guide the future development and application of MXene–polymer nanofibers in biomedical fields. MXenes were first invented by Professors Yury Gogotsi and Michel W. Barsoum with their group at Drexel University in 2011 [MXenes are the recently emerged member of multifaceted two-dimensional 2D) transitional metal carbides and nitrides, which are derived from MAX phases, where M represents layers of transition metals, A represents A-group element (mostly IIIA and IVA), and X represent either carbon or nitrogen . They arD transit in 2011 .3C2) by chemically etching MAX phases of Ti3C2 using HF [3AlC2 MAX phase into the Ti3C2Tx MXene is given in Typically, MXenes are synthesized via the selective removal of the “A” layer from their MAX phase by etching. Due to the stronger chemical bonds between the Mn + 1Xn layer and the “A” element layer of MAX phases, it is difficult to prepare MXenes by mechanical exfoliation. However, various mix bonds in the M-A layer  are weaker than the metallic bonds of the M-X layer. This feature enables the possibility of selective removal of the A layer from the parent phase by etching. In 2011, Gogotsi, Barsoum, and colleagues first synthesized titanium carbide  can make them semiconducting . The eleThe mechanical properties of MXenes are closer to various 2D materials . It has been found that various factors such as the atomic layer thickness, porosity, interlayer spacing, the method of preparation, surface terminations, and composition influence the mechanical properties of the MXenes ,39. The As MXenes can be composed of a wide variety of transition metals, MXenes can possess magnetic properties. Most of the reported magnetic MXenes are based on magnetic transition metals . In addi3C2Tx has been evaluated. Simulation studies have shown low thermal expansion coefficients and higher thermal conductivities of MXenes compared to that of the phosphorene and MoS2 monolayer [So far, thermal conductivity of only Tionolayer ,44.MXenes show strong plasmonic resonance, broad optical transparency window, nonlinear optical performance, transparency, photothermal conversion, etc. It should be noted that these properties are also affected by the functional groups. For example, fluorinated and hydroxyl terminations show similar characteristics, contrasting with oxygen ones. In the visible range, -F and -OH terminations reduce absorption and reflectivity, whereas, in the UV region, all terminations enhance the reflectivity as compared to the pristine MXenes . The MXeIn recent years, MXenes have gained tremendous research interest due to their fascinating physiochemical properties . These p3AlC2 with poly (PDDA) and polyvinyl alcohol (PVA) to produce a composite. Since then, several research groups have prepared MXene–polymer composites for various applications. So far many polymers have been reported, such as polyacrylic acid (PAA) [Preparing composites is considered an effective strategy to develop stable and efficient materials. The composite materials showed improved electronic, magnetic, mechanical, thermal, optical, and structural properties for advanced applications. As discussed earlier , severalid (PAA) , PVA [61id (PAA) , polyethid (PAA) , polyethid (PAA) , polyproid (PAA) , polystyid (PAA) , polyimiid (PAA) , acrylamid (PAA) ,69, siliid (PAA) , urethanid (PAA) , and epoid (PAA) , etc. Geid (PAA) ,73,74. Aid (PAA) ,75,76. TElectrospinning is known as a simple, versatile, and cost-effective technique for producing nano-to-microfibers from various polymeric solutions or melts . The ele3C2 for the first time and studied the effect of addition of MXene on the structure and properties of the nanofibers. The authors found that the MXene nano-flakes can be encapsulated into the polymer nanofibers by electrospinning technique. Since then, several research groups have incorporated MXene sheets into polymer nanofibers for various applications [MXenes have gained attention as recently discovered two-dimensional (2D) metal carbides/nitrides ,108. Preications ,116,117.In recent years, several efforts have been made to incorporate the MXene sheets into polymeric nanofibers for biomedical applications such as antibacterial, biocompatibility, biosensing, wound healing, and bone regeneration, etc. . From Ta3C2 MXene composite nanofibers by electrospinning for the first time and studied their physiochemical and biological features. The authors prepared Ti3C2 MXenes by hydrofluoric acid (HF) etching, dispersed them into PCL solution, and finally electrospun the solution into MXene–PCL composite nanofibers . The MXene composite nanofibers revealed good biocompatibility and enhanced cellular activities. MXene–polymeric nanofiber membranes are also effective in promoting spontaneous osteogenic differentiation [Biocompatibility is a prerequisite for any materials for biomedical applications. It should enhance tissue regeneration without inducing any adverse reactions . Awasthinofibers . They str et al.  determing et al. . They prntiation . Recentlntiation  synthesi3C2Tx MXenes as a new family of 2D antibacterial materials for the first time. Recently, some efforts have been made to synthesize antibacterial polymeric nanofibers by incorporating MXene sheets via an electrospinning technique [E. coli) and Staphylococcus aureus (S. aureus) bacteria  by an electrostatic spinning process. The composite nanofibers showed excellent antibacterial activity (higher than 99%) against E. coli and S. aureus. According to the authors, the excellent antibacterial activity could be due to the combined effect of reactive oxygen species (ROS) and hyperthermia induced by light irradiation.Implant-related infection is a serious issue in the healthcare system which may lead to acute/chronic inflammation and foreign body reaction resulting in microbial colonization and infection. Developing antibacterial biomaterial is a promising strategy to prevent implant-related infections. Several studies have shown that the antibacterial property can be induced into the electrospun polymeric nanofibers by loading the antibacterial agents ,134,135.echnique ,129,132.echnique  incorporbacteria . After 42C2Tx) has been used mainly for wound healing due to the antimicrobial and biocompatible nature of titanium [E. coli and S. aureus, respectively. The wound healing was tested on mice model and the results showed that the nanofiber membrane was effective in enhancing the wound healing rate upon laser irradiation. Diedkova et al. [3C2Tx MXene immobilized polycaprolactone (PCL) nanofibers and studied their structural, chemical, electrical, and biological properties. It was observed that the double and triple coatings on PCL fibers showed good performance for attachment and proliferation with antibacterial properties. The obtained results from this study demonstrated the potentiality of the MXene–PCL nanofibers for tissue engineering.In recent years, electrospun nanofiber membranes have been extensively investigated for wound-healing applications. Electrospun nanofibers can be designed in two-dimensional (2D) and three-dimensional (3D) configurations  which cltitanium . Howevertitanium . Loadingtitanium  prepareda et al.  preparedAchieving effective and fast wound healing is a great challenge. Recently, drug- or biomolecule-incorporated nanofibers have been explored for rapid wound healing and other biomedical applications ,146,147.3C2Tx)/poly(vinylidene fluoride trifluoro ethylene) (PVDF-TrFE) composite nanofibers sandwiched in between biocompatible poly-polystyrene sulfonate/polydimethylsiloxane electrodes and exploited them as a wearable capacitive pressure sensor. It was observed that the sensitivity of the nanofiber-based sensor was enhanced by MXene loading. According to the authors, the sensor can be used to determine the health condition of patients by inspecting various physiological signals such as pulse rate, respiration, movement of muscle, eye twitching, etc. Similarly, Leong et al. [3C2Tx doped polypyrrole on flexible electrospun PVDF nanofibers. Thus, prepared electrospun composite fibers provide a percolation for conductive filler network formation, which is the key factor of excellent sensitivity. The sensor can be adapted for real-time human motion detection. Cui et al. [2/day, respectively. Furthermore, the study showed that the as-designed e-skins can be continuously used for at least seven days.The development of wearable electronics has become an established issue in the healthcare system. Some of the exciting features of the MXenes such as large surface area, electrical conductivity, excellent piezoresistive behavior, and solution processibility make them suitable candidates for wearable sensing applications ,148,149.g et al.  preparedi et al.  demonstrRecently, the incorporation of MXene into various polymeric nanofibers has been prepared for various applications. This review discussed the synthesis of MXene–polymeric nanofibers membranes by electrospinning technique and their biomedical applications. The mechanical, physiological, and biological properties of the electrospun nanofiber membranes are important characteristics for biomedical applications. Designing composite nanofibers with MXene sheets further enhanced these properties, widening their applications in biomedical fields. The MXene–polymer nanofibers composite has shown promising candidacy in antibacterial, wound healing, cellular differentiation, bone tissue regeneration, neural tissue guidance, and health monitoring systems. However, the toxic and corrosive liquids used for the synthesis of MXene may bring undesirable impacts. In addition, it is challenging to disperse MXene sheets into polymeric solution and prepare nanofibers with well-distributed MXene sheets. It should be noted that most of the studies have used titanium carbide as a filler material in polymer nanofibers, whereas the other MXenes are yet to be explored in electrospinning systems for biomedical applications. The practical applications are another issue. So far, there are very few reports on such nanofiber composites, most of the studies are at the lab scales, and further investigations are needed for the clinical applications. A comprehensive understanding of various MXenes and polymer nanofiber forming by electrospinning is essential to address the above issues. Therefore, further research works are needed to overcome the aforementioned problems.


Antimicrobial resistance (AMR) is a global and increasing threat to human health. Several genetic determinants of AMR are found in environmental reservoirs, including bacteria naturally associated with widely consumed fermented foods. Through the food chain, these bacteria can reach the gut, where horizontal gene transfer (HGT) can occur within the complex and populated microbial environment. Numerous studies on this topic have been published over the past decades, but a conclusive picture of the potential impact of the non-pathogenic foodborne microbial reservoir on the spread of AMR to human pathogens has not yet emerged. This review critically evaluates a comprehensive list of recent experimental studies reporting the isolation of AMR bacteria associated with fermented foods, focusing on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities. Overall, our analysis points to the methodological heterogeneity as a major weakness impairing determination or a causal relation between the presence of AMR determinants within the foodborne microbial reservoir and their transmission to human pathogens. The aim is therefore to highlight the main gaps and needs to better standardize future studies addressing the potential role of non-pathogenic bacteria in the spread of AMR. Salmonella, Listeria, and Escherichia coli as incidental food contaminants cannot be excluded , and limiting the time span to articles published between 2018 and 2023. This initial search retrieved 120 research articles, but we restricted the final list to 49 articles following an accurate reading of the text, as they appeared to be the most relevant for the aims of the present review. We then focused in particular on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities.The fermented food matrices analyzed in the articles, along with their geographical origin, are reported in Overall, the great majority of studies reported the identification of AMR bacteria in animal-based fermented food products, including both dairy and meat/fish sources a. HoweveWhen compared to the geographical distribution of the studies , the preAs shown in the The power of molecular approaches is increasingly being exploited to investigate AMR in foodborne bacteria, but the identification of AMR determinants by genome sequencing alone is not sufficient to assign the AMR phenotype to a specific strain or isolate, as it does not provide information on the actual expression of the detected gene(s). As mentioned above, four of the analyzed articles did not test phenotypic resistance to the antibiotics corresponding to the detected AMR determinants, whose presence was identified exclusively by sequence comparison. On the other hand, 21 of the articles reporting phenotypic resistance of the selected isolates to one or more antibiotics did not proceed to the molecular identification and characterization of AMR genes. In both cases, the interpretation of the data in terms of the potential effects of gene transfer to other bacteria is impaired. Overall, simultaneous detection of phenotypic and genotypic AMR, which represents the most informative approach, should be considered a standard procedure. However, as shown in tetA, B, K, L, M, O, S, W) and erythromycin  resistance genes, confirming the results reported in previous studies in foodborne LABs [As for the molecular identity of the detected AMR determinants, there was a clear prevalence of tetracycline  focused on a single (or very few) specific AMR species/strains that were pre-selected for further characterization among the foodborne isolates, thus impairing full comprehension of the biodiversity associated with AMR within the food matrix. Finally, two of the studies employed metagenomic analysis of the whole microbial community within the food samples, which was not accompanied by the isolation of AMR bacteria. In these latter studies, the identification of the dominant species in each food sample and the corresponding presence of AMR genes were thus performed independently based on sequence analysis. Although highly informative of the fine structure of the microbial community, this approach does not allow for the precise assignment of AMR determinants to specific taxonomical entities. Lactiplantibacillus was the most represented genus hosting AMR determinants, closely followed by Enterococcus. Pediococcus, Lactobacillus, Streptococcus, Lactococcus, and Lacticaseibacillus, which are commonly found within the fermented food microbiota, were also frequently isolated. Accordingly, Staphylococcus and Latilactobacillus, which are mostly represented in meat-based foods that were a small minority of the products analyzed in all countries, were identified in only three studies. Further analysis of the occurrence of each AMR genus within the three major food matrices  confirmed this observation  did not verify the transferability of AMR genes, although two of them ,28 mentiSince HGT represents a key element in the spread of AMR genes among bacteria, we analyzed in depth the nine articles reported in E. faecalis JH2-2). However, the results of in vivo mating were described only in terms of the positive recovery of transconjugants without providing the corresponding transfer frequencies, while in vitro conjugation resulted in a transfer frequency of 10−6. We cannot therefore compare the results obtained with the two experimental approaches. As for the transfer frequencies reported in the nine articles, they were highly variable among the different studies, spanning from 10−1 to 10−9. Overall, low frequencies were observed in most cases, ranging from 10−6 to 10−7, but some articles reported higher transfer frequencies (10−1–10−2), especially in food mating systems [E. faecalis strain JH2-2. However, several other different pathogens were used as recipients, for example, S. aureus, K. pneumoniae, E. coli, and L. monocytogenes. In one case, a LAB strain was used as a recipient  or Gram-negative , human pathogens, or opportunistic pathogens. In all cases, the donor strains were represented by foodborne LAB, whose isolation from the different food matrices is described in All nine articles summarized in  systems . Such an systems ,33). Ove8647, in ), but alLactobacillus genus and by Enterococcus faecalis and faecium species, followed by Pediococcus pentosaceus and Staphylococcus saprophyticus. Concerning the choice of specific resistance determinants, transfer of tet and erm genes was prevalent, in particular tetM and ermB, respectively. This could be related to the frequent association of such genes with the conjugative transposon Tn916, conferring high transmission capacity [tet and erm genes carried by the same isolates but not associated with transposons or other conjugative elements did not show any transfer to the recipient strains [As shown in capacity ,76. Inde strains ,64.E. faecalis strains in two articles performing in vitro mating on filter or solid agar [P. pentosaceus strain OBK05 to four different Gram-positive pathogens was also shown by mating assays within a cheese matrix and was hypothesized to be mediated by a plasmid. However, the corresponding resistance gene was not identified but was only supposed to be plasmid-encoded since plasmid curing impaired the acquisition of the resistant phenotype by the recipient strains [tet and erm genes: for example, the ermB, tetM and tetS genes were successfully transferred from L. delbrueckii ssp. bulgaricus strain R6 and Lactiplantibacillus plantarum strain R41 to Listeria monocytogenes, but when using 18 other donor strains no transfer of the same genes was observed toward the same recipient [tetK was successfully transferred from the Staphylococcus saprophyticus strain KM1053 to two out of four Enterococcus/Staphylococcus recipients [E. faecalis and E. faecium donors to the recipient E. faecalis strain JH2-2 [msrC was reported to be mobilized from E. faecalis and E. faecium donors to the E. faecalis strain JH2-2 recipient by filter mating [P. pentosaceus isolates to the same E. faecalis JH2-2 recipient within food matrices were unsuccessful [tet and erm genes that were shown to be associated with plasmids or transposons (Tn916) and to be capable of horizontal transfer [tetL, harbored by the E. faecium strain UC7251, which was not transferred to Gram-negative recipients, probably due to the non-conjugative nature of the plasmid, which was lacking a complete conjugation apparatus [Aminoglycoside and streptomycin resistance genes were shown to be capable of HGT in lid agar ,64. Hori strains . Noteworecipient ; tetK wacipients , while iccessful . Aside fccessful ,45,64, wtransfer ,43,60,64Another methodological aspect that should be better standardized concerns the techniques used to confirm the transconjugants. Indeed, phenotypic AMR as the only confirmation method is often unreliable, and, as suggested by other authors, the combination of molecular analysis with phenotypic tests is recommended . Among tIn conclusion, our attempt to perform a comparison of the relevant literature published in the past five years and reporting the isolation and molecular characterization of foodborne AMR bacteria points to the methodological heterogeneity as a major weakness preventing a causal relationship between the presence of AMR determinants within the microbial reservoir associated with fermented foods and their transmission to human pathogens. This important limitation affects, in turn, the possibility of assessing the actual risk posed by the foodborne environmental reservoir for human health, which represents the basis for developing policies aimed at preventing the uncontrolled spread of AMR.Effective action to tackle the spread of AMR requires constant monitoring. Within the EU, the European Food Safety Authority (EFSA) coordinates annual monitoring of AMR in pathogenic bacteria from food and livestock and provides scientific advice on risk assessment models and possible strategies to counteract the environmental increase of AMR bacteria . The useAt a global level, the AMR Inter-Departmental Working Group established by the Food and Agricultural Organization (FAO) in 2015 has recently delivered an updated action plan for the years 2021–2025 with several important objectives, including strengthening surveillance and research on AMR spread, promoting responsible use of antimicrobials, and enabling good practices. The document highlights the utmost importance of closing the gaps in current knowledge on the mechanisms of AMR spread and the urgency of addressing this growing global threat in all countries through a coordinated, multisectoral, One Health approach in the context of the UN 2030 Agenda for Sustainable Development .Within this context, the International Union of Microbiological Societies (IUMS) recently launched a call to action directed to all microbiological societies worldwide, highlighting the need to develop science-based policies aimed at preventing the spread of AMR while also preserving the microbial ecosystem and its biodiversity by developing sustainable solutions to control infectious agents . The oveFrom a policy viewpoint, tackling AMR as a One Health issue represents an extremely important step to raise stakeholders’ awareness and elicit a global response from the food and agricultural sectors in the fight against AMR. Moreover, the active involvement of inter-governmental organizations such as FAO, WHO, and OIE, which are supported worldwide, ensures the alignment of strategic and financial efforts to sustain science-based and international guidance in the fight against AMR spread.https://www.esfri.eu/glossary). The initial and crucial efforts addressed by the scientific community toward infrastructural development were therefore aimed at standardizing SOPs and pipelines to produce datasets that could meet the FAIR  principles. These standardization efforts have impacted microbiological research as well [From the scientific viewpoint, further investigations are needed to close the knowledge gaps, but their usefulness relies on the possibility to compare the results of studies performed in different settings on different foods and microbial environments, which requires a high degree of methodological standardization, pointing to the urgent need for aligning standard operating procedures (SOPs) and pipelines. Efforts in this direction have been in place at the European and international levels since the first decade of this millennium, resulting in the development of analytical protocols for the quantification of AMR in food-grade bacteria. Factors affecting the results of phenotypic AMR assays, such as the composition of the growth medium, the inoculum size, and the time and temperature of incubation, have been identified, and the experimental protocols for determining the minimum inhibitory concentration (MIC) for antibiotics are presently applied with fully standardized procedures. Reference cut-off values to discriminate resistant and susceptible strains have also been uniquely defined in a document published and periodically updated by EFSA . Highlig as well ,85,86. F


Rapid RAFT polymerization can significantly improve production efficiency of PAM with designed molecular structure. This study shows that ideal Reversible Addition–Fragmentation Chain Transfer (RAFT) polymerization of acrylamide is achieved in dimethyl sulfoxide (DMSO) solution at 70 °C. The key to success is the appropriate choice of both a suitable RAFT chain transfer agent (CTA) and initiating species. It is illustrated that dodecyl trithiodimethyl propionic acid (DMPA) is a suitable trithiocarbonate RAFT CTA and is synthesized more easily than other CTAs. Compared to other RAFT processes of polymers, the reaction system shortens reaction time, enhances conversion, and bears all the characteristics of a controlled radical polymerization. The calculation result shows that high concentrations can reduce high conversions, accelerate the reaction rate, and widen molecular weight distributions slightly. This work proposes an excellent approach for rapid synthesis of PAMs with a restricted molecular weight distribution. Polymers that dissolve or swell in water to generate solutions or dispersions are called water-soluble polymers . The hydPolyacrylamide (PAM) is the collective term for homopolymers and copolymers of acrylamide (AM) and its derivatives . It is ov/v) to achieve controlled polymerization of AM. However, the low monomer conversion, long reaction time, and tendency of amide monomers to poison the metal catalysts in the ATRP solution hinder the achievement of controlled/reactive radical polymerization.Controlled radical polymerization (CRP) techniques , includiRAFT polymerization proves itself to be an extremely versatile CRP technique. Rizzardo et al. ,17, fromIn the study of RAFT polymerization, the creation of water-soluble polymers by this approach has caught the interest of academic and industrial researchers; however, the synthesis of water-soluble polymers by RAFT polymerization has only been reported in industrial production on rare occasions. The following discussion will thus focus on the application of RAFT polymerization to the manufacture of water-soluble polymers. It demonstrates applicability to the controlled polymerization of a broad variety of monomers under a broad range of circumstances to produce materials with specified molecular weights, restricted molecular weight distributions, and sophisticated architectures ,27,28,29In this work, the CRP of AM in DMSO was performed using RAFT polymerization, and the kinetic characteristics of the polymerization reaction as well as the effects of monomer concentration and chain transfer agent concentration on the polymerization process are discussed.−1, a strong C=O stretch in 1715.3 cm−1 and a strong C=S in 1068.3 cm−1; all the signals indicate the existence of DMPA. 1H NMR spectrum of DMPA: 0.90 ppm , 1.25–1.40 ppm , 1.72 ppm , 3.27 ppm , showing that the desired DMPA has been achieved.For RAFT polymerization of AM, a suitable RAFT CTA is the primary consideration. Dodecyl trithiodimethyl propionic acid (DMPA) has been shown to be a good RAFT CTA that is more readily produced  than othv/v) as solvent, it was determined that the polymerization process could not occur in this mixture because 2–propanol acted as a transfer agent.Dimethyl sulfoxide (DMSO) was chosen as solvent for RAFT. After a number of fruitless efforts employing a mixture of 2–propanol and water  of PAM at various times during RAFT. The actual molecular weight of PAM is quite close to its theoretical molecular weight; the molecular weight distributions of the samples at various times are narrow (Đ < 1.3). This is consistent with features of CRP.The reaction rate of RAFT polymerization must be established based on the monomer conversion. Conversion was determined by ultraviolet (UV) spectrophotometer. The fluctuation of the absorbance of the ethanol solution of AM with concentration is seen in The kinetics of the polymerization reaction is one of the great indicators of whether the RAFT polymerization process matches CRP. The link between number average molecular weight and monomer conversion is seen in 0/[CTA]0/[AIBN]0 ratio was maintained at 200/1/0.1, and monomer concentrations of 12% and 20% were chosen to examine the impact of monomer concentration on the polymerization process  was bought from Changjiu Agri–Scientific Co., Ltd.  recrystallized from ethanol for two times. 2,2–Azobis(isobutyronitrile) (AIBN) as the initiator was supplied from Tianjin Kemiou Chemicals , recrystallized from methanol, and the purity was more than 99%. The solvents, including DMSO, methanol, ethanol, and acetone, were all purchased from Guanghua Chemicals  and used directly without any further purification. The water used in this work was distilled three times. The nitrogen had a purity of 99.99%.2 (7.7 g) and acetone (11.6 g) was slowly dropped into the flask. After stirring for 20 min, the solution in the flask gradually turned bright yellow. The reaction continued for 20 min, and the solution became clear from turbidity; chloroform (17.8 g) and NaOH aqueous solution  were then added. After 24 h of reaction, distilled water (150 mL) and concentrated hydrochloric acid  were added, and a large number of rice-like yellow particles appear above the solution. The yellow solid was washed in 300 mL isopropanol, and the filtrate was concentrated into a viscous paste, which was recrystallized in n–hexane three times. After drying, a yellow crystal was obtained at 16.18 g, which was the target product of dodecyl trithiodimethyl propionic acid (DMPA).The chain transfer agent, DMPA, was synthesized according to the literature following the reported procedure . Dodecan−1, 1715.3 cm−1, 1068.3 cm−1. 1H NMR: 0.90 , 1.25–1.40, 1.72 , 3.27 .Crystalline solid; IR (KBr): 3449.8 cm2 was passed into the solution for 30 min to remove O2, while the solution was heated to 70 °C. The designed amount of AIBN solution was then added, and the polymerization was processed under N2 for 2 h, then cooled in cold water, and a portion of the reaction solution was taken with a separatory funnel. After extraction, the precipitate was precipitated with acetone and dried under vacuum at 50 °C for 24 h. Polymerization kinetics and molecular weights were determined by GPC from the polymerization solution at predetermined time intervals, at which points the reacting solution was immediately quenched by immersion in ethanol. Conversions at different time were calculated by the absorbance at 242 nm between start to certain reaction time.The typical polymerization procedure was as follows: After dissolving the AM, DMPA, and DMSO in a flask, Nn and Mw, respectively, and molecular weight distributions (Đ = Mw/Mn) were determined by Gel Permeation Chromatography (GPC) using a Waters 515 pump, Ohpak KB–803 column and Waters 2410 refractive-index (RI) detector. The equipment was calibrated with polyethyleneoxide standards, and a phosphate buffer (pH = 6.5) at 0.10 mol/L was used as the eluent with 0.80 mL/min flow rate. Conversions of monomers (C) were determined by U–2010 spectrophotometer . 1H NMR (300 MHz) spectra were recorded on a Bruker Avance 300 spectrometer. A Nicolet MX–1E IR spectrophotometer was used to record the IR spectra in the scanning range of 4000 to 400 cm–1. Conversion was determined by ultraviolet (UV) spectrophotometer. The solution of AM was put into different 50 mL volumetric flasks, then diluted into different concentraioninto with 80% ethanol solution. Their absorbance was measured at 242 nm, and the absorbance–concentration curve was obtained as v/v) prior to analysis. Conversions were determined comparing the signal of the absorbs at 242 nm.Number and weight average molecular weights, Mn,th) to determine the success of RAFT. The measured and theoretical molecular weight and conversion data at different times are listed in n,th of PAM was calculated from the conversion of monomer, the initial molar concentration ratio of the monomer to CTA, the molecular weight of AM (71.08), and the molecular weight of DMPA (348.56). Mn,th at different reaction times can be calculated by the number of units of monomer converted into polymer, as shown by Equation (1):Additionally, it was necessary to compare the measured molecular weight and theoretical one  with DMPA as the CTA. According to our findings, a high concentration not only decreases the high conversion and quick velocity, but also results in a somewhat wider molecular weight dispersion. It is evident that the procedure that was reported in the current work offers a substantial gain in the capability to achieve high conversion in a short amount of time, therefore preparing a block of PAM to be included in a future investigation.This study examines several factors for RAFT polymerization. The chain transfer agent facilitates RAFT, and polymerization reduces double group termination, controlling molecular weight. Excess free radicals cannot completely react with CTA to create inactive intermediates. RAFT polymerization at 70 °C has a higher molecular weight, according to studies. When the initiator concentration is 0.14 g/L, it is clear that the generated PAM has a higher molecular weight. When the molar ratio of CTA to AIBN is 10, the synthesis of PAM is accelerated and the molecular weight of the PAM synthesized is higher.