This Special Issue aims to highlight the advances made regarding the molecular profile of digestive system tumors in experimental and clinical studies. We have included data regarding the gastrointestinal tract, liver, and pancreatic tumors.The most addressed tumor type was colorectal cancer (CRC). Banias et al.  performeThe molecular classification of CRC can be based on immunohistochemical and molecular biomarkers, such as keratins 7 and 20 and BRAF  gene status, which can be used to identify serrated carcinomas  or maspiSeveral studies have focused on new prognostic or predictive parameters, and new pathways are frequently described for colorectal carcinogenesis. One new biomarker is the spindle apparatus coiled-coil protein 1 (SPDL1). Klimaszewska-Wisniewska et al. proved that SPDL1 upregulation in CRC cells indicates chromosomal instability and long survival . YasudomAll the above-mentioned biomarkers appear to possess prognostic and predictive value for CRC. In addition to 5-Fluorouracil, cetuximab, bevacizumab, and other molecular-targeted drugs used in oncologic regimens for CRC, it has been suggested that natural flavonoids, such as the propolis phytoestrogen known as chrysin, might have anticancer properties by inducing G2/M phase arrest of tumor cells .Regarding pancreatic cancer, Lim et al.  proposedOne of the newest molecular approaches for hepatocellular carcinoma (HCC) involves damaging tumor cells through the opposite mechanism of hepatocarcinogenesis. Based on this approach, Huang et al.  examinedHER-2. Several patients with metastatic gastric carcinomas with HER-2 gene amplification have benefited from trastuzumab therapy. However, the amplification rate is low, and HER-2 heterogeneity increases the risk of therapy resistance [Therapeutic challenges are also associated with gastric cancer. Although several studies have focused on its genetic profile, the only suitable gene target for inhibition is sistance . Based osistance  proposedsistance . Koustassistance .Sarantis et al.  investigThe molecular pathways that induce the development and progression of digestive malignancies are complex and require further elucidation. Molecular parameters are useful for enabling better stratification of cases by offering both prognostic and predictive value. However, further studies are needed to understand how to therapeutically inhibit digestive carcinogenesis independently based on tumor location.


Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database. Identification of chemical compounds, present in a sample, is a fundamental task of chemical analysis, but matching obtained spectra with chemical databases is limited to identifying known molecules. Here, the authors report a deep learning architecture for recommending molecular structures, including those of novel molecules, given sample mass spectra alone. Additionally, mapping the human metabolome will lead to a better understanding of human physiology and disease etiology and pathology which is essential for the identification of new therapeutic targets for developing new treatments. The increasing interest in mapping the metabolome extends to other organisms as well, such as plants which have been a great source of bioactive compounds for multiple products including drugs and supplements2. The identification of chemical compounds is also critical in product development such as in the production of pharmaceuticals and agrochemicals. Structure elucidation practices are being used for quality control and detection of impurities, as well as in safety studies for identifying potential metabolites that can be formed in the human body. Finally, structure elucidation practices are being employed in forensics analysis.The identification of the chemical compounds that are present in a sample of chemical matter is a fundamental task in chemical analysis with applications in multiple domains. The field of metabolomics, for example, seeks to identify the chemical molecules that are present in a biological sample. In humans, the metabolome, that is the set of all chemical molecules that can be found in human tissues, is a great source for biomarker discovery as it reflects changes at a genetic, proteomic or environmental level5 with MS being used more often due to its higher sensitivity and specificity3. In MS, the molecules that are present in a biological sample are first separated using a chromatographic technique, such as liquid chromatography (LC) and gas chromatography (GC), with the latter being used more commonly6. After the separation, the molecule is fragmented into positive or negative charged ions using an ionization source such as electron ionization (EI), chemical ionization (CI) and electrospray ionization source (ESI)6. What the instrument records is the mass-to-charge (m/z) ratios of the generated fragment ions. The information that is collected from this process is presented in the mass spectrum which is a graph with the m/z of each recorded fragment in the horizontal axis and the relative abundance in the vertical axis. In order to obtain more detailed information on the query structure, a sequential fragmentation process is often used called tandem mass spectrometry5. Once the molecule has been fragmented into ions, a set of them, called precursor ions, is selected and further fragmented to generate MS2  spectra. These second-level ions can be fragmented even further giving MS3 spectra and so on. The peaks and their intensity in the resulting spectrum depend not only on the structure of the chemical molecule that is being fragmented, but also on the experimental conditions, that is the instrument used, the collision energy, the selected precursor ion and the ionization mode, as it is illustrated in Fig. The identification of the structure of a chemical compound is perceived as one of the most time consuming and laborious task in chemical analysis. This is often performed through analytical techniques such as mass spectroscopy (MS) and nuclear magnetic resonance (NMR)7 such as Human Metabolome Database, METLIN, MassBank, and mzCloud7. Certain databases are focused on the metabolites of specific organisms, such as the Human Metabolome Database, or on specific molecular classes, such as the LIPID MAPS Structure Database, while others have greater coverage such as METLIN. However, despite the intense ongoing efforts to map the metabolome of various organisms, existing databases cover only a small percentage of the actual metabolites that occur in organisms. Particularly for humans, it is estimated that less than 10% of metabolites have experimental reference mass spectra8, which means that the current practice cannot identify a large percentage of the molecules that are found in human tissues. It is estimated that in untargeted metabolomics studies less than 2% of the detected spectral features are identified8.Once the mass spectrum is obtained, it is matched against the content of spectral databases of reference compounds in order to retrieve its structure. Various chemical databases provide spectra data of metabolites10. Fragment prediction methods have been especially successful for predicting spectra of peptides, however, fragmentation of small molecules into ions is a more stochastic process that is especially challenging to predict6.An approach that has been developed to address the problem of limited amount of experimental spectra data is in silico fragmentation which essentially attempts to solve the inverse problem. This approach aims at enhancing the content of existing spectra databases with computed spectra of known molecular structures which have no available experimental spectra. Essentially this approach seeks to close the gap between spectral and structural databases. In silico fragmentation tools predict the fragmentation process either relying on fragmentation rules or using combinatorial/optimization-based approaches or employing machine learning methodologies12. This method though will also fail for molecules that are not present in the structural database and especially for novel molecules. A more direct association of spectra features with molecular structures through a rule-based approach has also been explored13. More specifically, this approach extracts rules, that associate spectra features with substructures, from spectra databases aiming at a partial structure identification.A more direct approach to the structure elucidation problem would be to reconstruct the underlying chemical structures given spectra features. Such an undertaking though is computationally challenging as it requires the generation of a molecular structure. Indeed, this approach is performed as a two-step process to circumvent the need for generating molecular structures: A machine learning model is used to map the spectrum to an intermediate vector representation such as a molecular fingerprint. Once the fingerprint is obtained then it is matched against the content of structural databases in order to identify candidate molecular structures with similar fingerprints14. A fragmentation tree is derived computationally from tandem mass spectra using optimization algorithms such that its nodes correspond to fragments or precursor ions and the edges correspond to fragmentation reactions. Fragmentation trees have various uses such as identifying the molecular formula and clustering molecules by aligning fragmentation trees15. They have also been used for the prediction of molecular fingerprints that are subsequently used to search structural databases17. The information in a mass spectrum is thought to be insufficient to explain the fragmentation process by itself while the fragmentation tree provides complementary information by elucidating the dependencies between the mass peaks6. However, fragmentation trees are expensive to compute and often approximations are preferred for practical applications.An additional concept that has been introduced to facilitate the interpretation of mass spectra, and subsequently structure identification, is that of fragmentation trees6. It should be noted here that early ML-based approaches were built on shallow ML models, such as Support Vector Machines (SVMs) and Random Forests (RFs), applied either on features extracted from the mass spectra or the fragmentation trees, and also kernel-based methods to determine similarity between either spectra or fragmentation trees. However, lately, there is a growing interest in exploring Deep Learning (DL) architectures for the development of computation tools to support structure elucidation. There have been efforts to learn spectra embeddings that can be subsequently used to assess spectral similarity when searching in spectral databases18. Additionally, there are DL-based methodologies for clustering spectra, either for identifying the compound class19 or for aiding medical diagnosis by differentiating between healthy and cancerous tissues20. Most DL-based methodologies that operate directly on spectra data are based on Convolutional Neural Networks (CNNs) representing the spectrum as a vector that indicates the intensities of each fragment mass22. The CNN attempts to automatically identify spectra features replacing the need for manual featurization. Architectures that have adopted concepts from Natural Language Processing (NLP) have also emerged representing the mass spectrum as text and the mass peaks as words18. Due to the limited amount of mass spectra data, different workarounds have been investigated including hybrid approaches19, combining statistical ML models and DL architectures, and approaches based on transfer learning20.A more thorough review of existing methodologies for metabolite identification, including in silico fragmentation tools, fingerprint prediction, and fragmentation trees, was recently presented by Nguyen et al. with a focus on machine learning (ML) approaches23. A noteworthy effort, DeepNovo, consists of an end-to-end DL architecture for de novo peptide sequencing from mass spectra22, which is a direct reconstruction of the peptide sequence from the mass spectra data. Structure elucidation of small molecules though is perceived as a more challenging problem due to the stochastic nature of the fragmentation process. On top of that, the structure of small molecules has a graph-like representation as opposed to the linear nature of a peptide sequence. Existing approaches essentially attempt to retrieve molecules from structure databases that have a spectrum similar to the query spectrum. This method though, cannot identify novel molecules, that is molecules whose structure currently remains unknown and therefore they do not exist in chemical databases.It should be noted that, at the same time, DL-based approaches are being investigated for identifying protein sequences from mass spectra in proteomics studies24 and MS/MS spectra as vectors of fragment intensities. Spec2Mol consists of an encoder, that learns an embedding for the MS/MS spectrum, and a decoder that generates the SMILES sequences of the recommended chemical molecules. Due to the limited amount of available spectra data, our approach is based on unsupervised pre-training on a large dataset of unlabeled molecules. In particular, we pre-trained the decoder as part of an auto-encoder (AE) architecture which is trained to reconstruct a molecule through its SMILES sequence. The encoder is subsequently trained such that the spectra embeddings match the embeddings that the AE has learned. In the following sections, we discuss the data used to develop and evaluate the model, the architecture of Spec2Mol, as well as, the evaluation of the model.In this paper, we present Spec2Mol, an end-to-end DL architecture for translating MS/MS spectra to molecular structures. Spec2Mol is intended for recommending molecular structures that can explain observed MS/MS spectra. We represent molecular structures as sequences using the SMILES notationTo our knowledge, this is the first approach for generating potential molecular structures from mass spectrometry data that is not based solely on database retrieval.Our method can facilitate database retrieval and additionally de novo molecular structure recommendation.Our approach takes advantage of large datasets of unlabeled molecules using unsupervised pre-training.We introduce metrics to assess the similarity of the generated molecules with the reference ones and we perform a comparative evaluation with a widely accepted method that makes use of additional information, that is fragmentation trees.The main contributions of this work are as follows:27, has been successfully used in similarity search and have been found to be more informative, when compared to molecular fingerprints.As a sanity check, we evaluated the ability of the pre-trained AE to reconstruct the SMILES of the molecules in the testing set of the spectra dataset. This is performed by comparing the canonicalized input SMILES and the canonicalized output SMILES and evaluating whether there is an exact match between the two. The autoencoder is trained by minimizing the mean reconstruction error on a single-character level for each input sequence. Therefore, the reconstruction accuracy is estimated on a single-character level, by comparing the correct character in the target sequence with the most probable character in the decoder RNN’s output at each position. It should be noted, that the reconstructed SMILES, as well as neural fingerprints derived from SMILESThe AE was able to correctly reconstruct the SMILES sequence for about 93.3% of the NIST molecules. This is very close to the reconstruction rate of the AE on a held-out test set which was 94.95%. This demonstrates that the pre-trained model has been trained on a diverse set of molecules and therefore it is able to handle the large variability of the molecules in the NIST dataset.Spec2Mol generates a set of recommended molecular structures given MS/MS spectra. Our evaluation focuses on assessing the similarity between the generated structures and the reference molecular structure from the NIST dataset. We recall here that the information in an MS/MS spectrum may not be sufficient to fully reconstruct the molecular structure. It is possible that more than one molecular structures may explain a given spectrum. For that reason our analysis has been focused on assessing whether the model has learned to identify key features in the molecular structure from the mass spectra rather than identifying the exact same structure with the reference molecule from the NIST dataset.Physiochemical attributes: A property of special interest is the molecular weight since it is directly reflected in the mass spectrum. In particular, the spectra indicates the mass of the fragments and therefore the mass of the original, non-fragmented, molecule can be approximated more easily given the mass spectra as opposed to determining the composition or the structure of the molecule. We record the difference between the molecular weight of the generated structures and the reference structure and we report the relative average-minimum difference, that is, the average-minimum difference over all the predicted structures divided by the average molecular weight of the reference structures (avg). Additionally, we also evaluate whether the model is able to identify the element composition of the molecule. In particular, we assess whether the atom species that are present in the reference molecule have been identified in the predicted structures ignoring the numbers of atoms for each atom species. More specifically, for each atom species we report sensitivity and specificity for detecting the presence of this species. In order to account for discrepancies in the number of atoms per atom species, we also report the difference between the molecular formulas of the predicted structures and the reference structure (DMF). We define the distance between two molecular formulas as the number of atoms that differ between two molecules when accounting for the atom species and the number of atoms for each species (without including hydrogen atoms). We report the minimum distance over all predictions divided by the average number of heavy atoms (avg). The exact mathematical formulas for the calculation of the DMW and DMF are provided in the supplementary material . The Morgan fingerprints are computed for radius 2 and 1024 bits. We report the maximum fingerprint similarity among all model predictions when compared with the reference structure as well as the average similarity of all predicted structures.Maximum common substructure (MCS): We computed the MCS between two molecular structures using the RDKit toolkit30 with the following constraints: the substructure match respects the atom species, the bond orders, as well as the ring bonds, that is ring bonds are only matched to ring bonds. From the computed MCS we extracted the following three metrics: i) MCS ratio, ii) MCS Tanimoto, and iii) overlap coefficient, which are defined as follows, respectively: MCS denotes the number of atoms in the MCS, ar the number of atoms in the reference compound, and ap the number of atoms in the predicted compound. For each metric, we report the maximum value as well as the average value over all predictions.For the evaluation of the model, we first perform a coarse-level comparison taking into account physicochemical properties and more specifically the molecular weight and the element composition of the molecule. Next, we assess molecular similarity at the substructure level. In particular, we compute the fingerprint similarity as well as the maximum common substructure between the generated structures and the reference structure. The specifications for each metric are given below. We evaluate the overall performance in the entire test set as well as the performance of the model when not all four required spectra are available as input. Additionally, we assess the contribution of each of the two strategies for generating the recommended structures.Table Next, we evaluate the effect of the strategy that is used to generate the recommended molecules. The analysis is shown in Table Overall, the results illustrate that the predicted structures have a molecular weight that is significantly close to the molecular weight of the reference compound. This is not surprising as the generated molecules are ranked based on the molecular weight. The molecular formula though seems to also be considered close to the reference one. The model was able to retrieve the exact structure for a small percentage of the test cases (7%) while it identified the exact molecular formula for a considerably larger percentage (26%). The performance of the model was significantly better when at least 3 out of the 4 input spectra were available.Regarding the structural similarity between the predicted structures and the reference structure, the obtained values for the respective metrics demonstrate that the structures share common substructures. More specifically, the metrics that are based on the MCS between the reference and the predicted structures indicate that the common substructure is, on average, nearly 70% of the size of the reference structure for the closest structure and more than 50% for the average prediction. This result is in agreement with the high correlation between the molecular fingerprints.Regarding the ability of the model to identify the presence of each atom species in the molecular structure, it varies significantly and it correlates with the frequency of each atom species in the training dataset, as it is shown in Table Finally, we investigated the effect of the molecular weight as well as the presence of heteroatoms on the ability of the model to identify the exact structure or the exact molecular formula. More specifically, we divided the test set molecules into those that have molecular weight (MW) less than 300Da and those that have molecular weight greater than or equal to 300Da (the average molecular weight in the test set is 275Da). Furthermore, we created four categories based on the presence of heteroatoms: (1) molecules that have only C and O, (2) molecules in which N is present, (3) molecules in which S is present, and, 4) molecules in which a halogen  is present. Table  moleculeFigure We also identify two general scenarios where the model has difficulty in predicting relevant structures: (1) Molecules with large rings and (2) Molecules that have poor-quality spectra. An example of the first case is illustrated in Fig. 31, which offers multiple functions including chemical formula, as well as molecular structure, identification from mass spectra. SIRIUS’ structure elucidation method, called CSI:FingerID, is a database retrieval method16. It relies on Support Vector Machines (SVMs) for predicting a molecular fingerprint and subsequently compares the predicted fingerprint against those of a reference database in order to identify candidate structures. The input to the SVM is the MS/MS spectrum along with the corresponding computed fragmentation tree. CSI:FingerID has shown superior performance when compared to other existing tools for the automatic identification of molecular structures from spectra data. In particular, it was the best-performing method in the Critical Assessment of Small Molecule Identification (CASMI) contest for 2016 and 201731. However, the performance of this method degrades significantly for cases that are not covered in the training set31. Additionally, the dependence of CSI:FingerID on fragmentation tree data adds significantly to the running time of this method.In order to perform a comparative evaluation, we have used SIRIUS 4We run SIRIUS on the same test set we developed for evaluating Spec2Mol. As input, we provided SIRIUS with the positive mode spectra (that is [M+H]+ at low and high energy) as they were selected for Spec2Mol. The spectra from negative ions were not used since a single run for SIRIUS accepts spectra from a single precursor which may be obtained through different energies. As 53 test cases out of the 1000 cases of the test set did not have any positive mode spectra and therefore the test set used for the comparison consists of 947 cases. As a side note, SIRIUS performs structure elucidation after identifying the molecular formula. The number of molecular formulas to be explored is one of the parameters of the tool which we set to 10. An additional parameter is the reference database which we set to PubChem, which is the largest available source offered by SIRIUS. Finally, SIRIUS allows the user to define the set of chemical elements to be considered when performing the search which we set to C, H, O, N, S, Cl, F, Br, P, and I. It should be noted that expanding the pre-defined set of atoms  to account for more rare atoms, which were present in the NIST dataset, significantly increased the running time.31.On the test set of 947 cases, SIRIUS found the correct formula for about 98% of the test cases while it found the correct structure for about 67%. For 6 cases out of 947, SIRIUS did not return any structures. It should be highlighted that the CSI:FingerID method from SIRIUS has been trained on the NIST dataset (NIST v17). As it is discussed in the original study on the SIRIUS tool, the presence of spectra for a given test structure in the training set can significantly boost performance even if the spectra that are used when testing is not the exact same spectra as the ones used in trainingThe comparative evaluation between SIRIUS and Spec2Mol was performed on the cases where SIRIUS failed to find the exact molecular structure. Since Spec2Mol is intended for recommending potential molecular structures given mass spectra, our intention here is to evaluate how relevant the recommendations are, when compared to a widely accepted and state-of-the-art method like SIRIUS. By focusing our comparison on the cases where SIRIUS did not find an exact match, we are essentially evaluating the relevance of the recommended structures when an exact match is not found, which points to the case of novel molecules. In particular, we compared SIRIUS and Spec2Mol on the 307 cases, for which SIRIUS failed to find an exact match, using the metrics based on fingerprint similarity and MCS. It should be noted here that failure to identify the exact structure includes cases where SIRIUS either did not return any structure as well as cases where the reference structure was not among the predicted structures. The results are summarized in Table Still, the outcome of our comparative evaluation demonstrates that the molecular structures generated by Spec2Mol are at least as successful as the ones obtained by state-of-the-art tools when considering novel molecules despite the fact that Spec2Mol relies solely on raw MS/MS spectra.32. A substructure-constrained similarity search or the nearest neighbor search on the embeddings of the molecule library with the spectra embedding as a query can be used to identify the best candidates from a relevant library.Elucidating the structure of chemical compounds is a fundamental, but cumbersome, task in metabolomics studies, as well as in chemical analysis in various domains including drug development and forensics analysis. The available computational tools for aiding structure elucidation are based on fragment annotation and database retrieval methods. This approach fails to identify molecules that are not present in the reference database which, in practice, may correspond to a considerably large percentage of the query spectra. We have developed Spec2Mol, an end-to-end deep learning architecture for directly generating molecular structures (as SMILES sequences) from the input MS/MS spectra. Spec2Mol is based on an encoder-decoder architecture that generates molecular SMILES sequences, given mass spectra. While the proposed architecture supports the retrieval of molecules from a database that best matches the input spectra, it can also generate new molecules that have not been seen before in any dataset. Our analysis demonstrates that the recommended molecules are structurally, and physiochemically, similar to the reference compounds, suggesting that the latent space has indeed learned informative associations between the spectra and the structural features. When compared to an existing method that depends on the fragmentation tree annotation, on top of the raw spectra for molecule identification, Spec2Mol performed on par for the task of recommending potential molecular structures. Our results indicate that the proposed approach of recommending de-novo molecules directly from input MS spectra provides critical insights into the characteristics of the underlying molecular structure, and, can complement existing tools especially when the current tools fail to identify the right molecule from existing databases. We speculate that incorporating prior knowledge in the model, for example in the form of fragmentation trees, can further boost the performance of the proposed method. Further, even though the main focus of our work is on de-novo generation of molecules given an input spectrum, the indirect method proposed by our paper can be extended to identify the correct molecule from a library of a plausible set of molecules, similar to the work proposed by Lim et al.Spec2Mol consists of an encoder that learns spectra embeddings and a pre-trained decoder, which has been trained as part of an autoencoder architecture. The autoencoder has been trained on a large set of molecules (molecule dataset discussed in section Molecule dataset), while the encoder has been trained on a set of molecules for which MS/MS data are available .33 and ZINC-1234 datasets. The structures of these molecules are represented using the SMILES notation24. Stereochemistry information was not indicated in the SMILES representation. The reason for not accounting for stereochemistry is that, in the subsequent task of spectra translation, recovering stereochemistry information from the mass spectra is especially challenging or possibly even impossible and therefore it is out of the scope of this work.The autoencoder, from which the Spec2Mol decoder has been derived, was pre-trained on about 135 million molecules which were sourced from the PubChem36. The largest percentage of the NIST dataset (60%) corresponds to metabolites (6K human metabolites and 8K plant metabolites) while a significant amount of the data is drugs (20%). The rest corresponds to peptides, lipids, forensics, surfactants/contaminants and sugars/glycans. The dataset contains low and high resolution MS/MS spectra, obtained through different fragmentation techniques. Each molecule in the dataset may be associated with more than one spectra which may be obtained through different experimental conditions, that is, different fragmentation instrument, precursor ion, ionization mode, collision energy or fragmentation level . Statistics of the dataset regarding common molecular properties , as well as the atom species coverage, are presented in the supplementary material  value, representing the m/z value of the recorded fragments, while the value of each bit corresponds to the intensity, or otherwise frequency, of the fragments that have been recorded with that specific mass-over-charge value. We have normalized the intensity values by dividing with the maximum intensity over all the vector bits of a given spectrum. More details on the representation of the MS/MS spectra are provided in the supplementary material  are shared. More information is provided in the supplementary material  which is a variation of the standard long short term memory (LSTM) models, that are commonly used for learning sequence representations, with fewer parameters. The details regarding the autoencoder architecture are in the supplementary material  and energy level (low or high). If any of the required four spectra are not available, then the input to the respective channel is an all-zeros vector. The output of the spectra encoder is a 1-D vector which is the latent representation of the spectra in the embedding space. The model is trained such that the distance (root mean square error) between the latent representation that is learned by the spectra encoder and the latent representation that is obtained from the pre-trained SMILES encoder is minimized. Details regarding the architecture and training of the spectra encoder are provided in the supplementary material (Supplementary Methods 37, and subsequently filtered in order to retain only the valid ones, i.e., the sequences that are in accordance with the SMILES syntax. The indirect strategy retrieves molecular structures from the dataset that was used for pre-training the AE based on the distance in the embedding space. More specifically, for each MS/MS embedding we find the closest embeddings from the pool of molecules used to pre-train the AE and decode those embeddings into SMILES sequences.Spec2Mol provides as output molecular structures that can potentially explain the observed spectra peaks. The recommended molecules for unseen spectra are obtained using two strategies: a direct and an indirect molecule generation strategy. The direct molecule generation strategy generates molecular structures using the SMILES decoder from the computed MS/MS embedding. Multiple SMILES are generated for each MS/MS embedding using a pure sampling strategyThe predicted molecules obtained through these two strategies are combined and ranked based on their discrepancy from the expected molecular weight. The molecular weight of the underlying chemical structure is easily inferred from the mass spectrum and therefore in this work we consider it as known. The molecular structures that have molecular weight closer to the reference weight are highly ranked. The top-20 ranked predictions are returned to the user.Supplementary material


It is widely accepted that, in order to prevent and control antimicrobial resistance (AMR), surveillance systems across human, animal and environmental sectors need to be integrated, in a One Health approach. Currently, in Europe, there are surveillance networks established only for the human and food sector and, until now, there has been no organized effort to monitor AMR in bacterial pathogens derived from diseased animals in Europe. Since 2017, efforts to fill this gap have taken place by the European Antimicrobial Resistance Surveillance network in a veterinary medicine (EARS-Vet) initiative, included in the EU Joint Action on AMR and Healthcare-Associated Infections (EU-JAMRAI). EARS-Vet is designed to complement and integrate with existing European monitoring systems for AMR as well as constitute a European network of national monitoring systems. As Greece has no national AMR surveillance system for pathogens of animal origin currently in place, in the context of the development of EARS-Vet, an initiative took place for the organization of such a system by competent agencies and other stakeholders. In this article, the steps to organize a first AMR national surveillance network in Greece are presented and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is performed to present main characteristics of the approach implemented. Salmonella and Campylobacter infections , are defined as those running monitoring procedures in practice  under the stewardship of the National Antimicrobial Reference Laboratory—Department of Veterinary Laboratory of Chalkis—Directorate of Veterinary Center of Athens—Directorate General of Veterinary Services—Ministry of Rural Development & Food.For the pilot project, the laboratory network responsible for the AMR investigation of isolates includes one central  and one private sector diagnostic laboratory; due to financial restraints, for the pilot project, one private sector laboratory is responsible for AMR monitoring in samples from the targeted animal species (cattle—swine), with the prediction of expanding the network as soon as financial support is available; the central public sector laboratory will not perform re-testing due to the same financial constraints but will play an advisor role to the private network (if expanded) for the harmonization of the AST methods implemented.The pilot project is primarily based on an active monitoring procedure, where diseased animals  are sampled specifically for the purpose of AMR monitoring and are submitted by field veterinarians to the private diagnostic laboratory. This active surveillance is considered to generate more representative AMR data than by passive monitoring; collecting passive data from veterinary diagnostic specimens tends to overestimate the AMR levels, as they are usually performed only after treatment failure . For theE. coli. Specifically, in cases where faeces are collected directly from the rectum using disposable gloves and sterile containers, a quantity of faeces of 1 gr is diluted in a quantity of 9 mL of diluent MRD (Maximum Recovery Medium). Then, an inoculation takes place from the dilution to sheep blood agar and MacConkey agar and the plates are incubated at 37 °C for 18–24 h. In most cases, faecal samples are collected directly from the rectum using sterile cotton swabs; inoculation is performed directly on the agar plates. Three phenotypically different lactose-fermenting colonies from MacConkey agar and three different colonies from blood agar are isolated on Nutrient agar. After incubation for 18–24 h at 37 °C, the suspected E. coli colonies are tested for indole production at 44 °C in tryptone water broth. For bovine samples, culture is performed immediately after receipt, to isolate Staphylococcus aureus. Regarding collection procedure, milk from dairy cows is collected by pooling samples from all udder quarters of each lactating cow. The udders are cleaned thoroughly with water, dried using a clean towel, and the teat ends are disinfected with cotton swabs soaked in 70% alcohol, then left to air dry. After discarding the first streams of milk, three to four streams (1–2 mL each) from each udder quarter  are collected into sterile leak-proof plastic containers. In the laboratory, a quantity of 1 ml of milk is diluted in a quantity of 9 mL of MRD. Then, inoculation is performed from the original sample on a sheep blood agar plate as well as inoculation of 50 μL of both the original sample and the dilution using the spread plate technique on Mannitol Salt agar plates, for better recovery capacity of the target microorganism. All plates are incubated at 37 °C for 24–48 h. Three phenotypically different mannitol positive colonies from the Mannitol Salt agar plates and three different beta-haemolytic colonies from the blood agar, are isolated on Nutrient agar. After 18–24 h of incubation at 37 °C, the suspected Staphylococcus aureus colonies are Gram stained and tested for catalase production. Gram positive & catalase positive cocci are tested for coagulase production by tube method with coagulase plasma.Laboratory techniques and standards of the Greek pilot project are described as follows: Collection of samples is focused on dairy cows and post-weaning piglets that exhibit relevant clinical symptoms . All collected faecal and milk samples are transported in an icebox and investigation is initiated within 24 h. For swine faecal samples, a culture is performed immediately after receipt, to isolate E. coli) and the coagulase-positive (S. aureus) colonies are subjected to a Kirby–Bauer sensitivity test. Specifically, a suspension is prepared from the confirmed colonies in 0.9% NaCl to obtain a turbidity of 0.5 McFarland units. Using a sterile cotton swab, the suspension is spread on a Mueller Hinton agar plate and the appropriate antibiotic disks, as reported previously and according to Clinical and Laboratory Standards Institute (CLSI) standards, are placed. Regarding the AMR interpretation of results, the Greek pilot project follows mainly the CLSI standards and interprets results according to its veterinary clinical breakpoints (when available) , (c) Hellenic Center for Disease Control and Prevention —Central Public Health Laboratory , (d) Special Water Secretariat (Ministry of Environment and Energy), (e) Asclepius One Health platform (Non-Profit Organization)—Chatham House .From the political sector: (a) Ministry of Rural Development & Food (consultant of the Minister’s Cabinet).Upon a request to explore the possibility of building a European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet) according to the EU JAMRAI Task 7.4.2  in GreecAfter this first kick-off meeting, the Ministry of Rural Development & Food (MRDF) defined a Coordination Team to organize the next steps. In Greece, there is a regulated system—a National Action Plan (NAP), published in 2019, for the period 2019–2023, to tackle AMR . In accoThe Coordination Team took a first decision as a next step, based on its experience/knowledge, that the first pilot project should be focused on cattle and swine samples. After this, the team sought help/feedback from a series of stakeholders related to the cattle and swine industry and research stakeholders: farm organizations, farm veterinarians, private laboratories representatives and central competent authorities. To achieve this, a call (via email) for a series of physical presence/teleconference meetings was made to all of the above-mentioned Greek stakeholders. During those meetings, representatives were asked to answer a semi-structured questionnaire covering key areas as suggested by the EARS-Vet  in orderFinally, after collecting all the relevant feedback from the various stakeholders, a one-day physical presence meeting took place in 2020 where relevant stakeholders and the members of the Coordination Team participated. During that meeting, all participants were asked to answer a semi-structured questionnaire based on the one designed by EARS-Vet  coveringThe Ministry of Rural Development and Food eventually launched a pilot project entitled “Trying to build our national surveillance system for the monitoring of the Antimicrobial Resistance in diseased animals (cattle and swine)”.The Greek pilot project on AMR surveillance in diseased animals showcases several strengths, including regular reporting, a flexible Coordination Team and successful collaboration among stakeholders. The focused scope and minimal harmonization contributes to its representativeness and its adaptable nature allows for future expansion. However, challenges such as the lack of formalization, limited resources and difficulties in data management hinder its full potential. Nevertheless, opportunities arise from the government’s prioritization of AMR, increased public awareness and potential support from the European Commission. Nonetheless, economic challenges in agriculture and the need for financial backing pose threats to its sustainability and data quality. Emphasizing the One Health approach is essential in addressing these weaknesses and leverage opportunities to enhance AMR surveillance in Greece, thereby safeguarding public health, animal health and the environment collaboratively.


The Lancet does not consider gender or other social stratifiers in its analysis. To address this evidence gap, we undertook a scoping review to examine how sex and gender compounded by other axes of marginalization influence one’s vulnerability and exposure to AMR as well as one’s access to and use of antimicrobials. We undertook a gendered analysis of AMR, using intersectionality as a concept to help us understand the multiple and overlapping ways in which different people experience exposure vulnerability to AMR. This approach is crucial in informing a more nuanced view of the burden and drivers of AMR. The intersectional gender lens should be taken into account in AMR surveillance, antimicrobial stewardship, infection prevention and control and public and professional awareness efforts, both donor and government funded, as well as national and international policies and programmes tackling AMR such as through national action plans.Different sexes and genders experience differentiated risks of acquiring infections, including drug-resistant infections, and of becoming ill. Different genders also have different health-seeking behaviours that shape their likelihood of having access to and appropriately using and administering antimicrobials. Consequently, they are distinctly affected by antimicrobial resistance (AMR). As such, it is crucial to incorporate perspectives on sex and gender in the study of both AMR and antimicrobial use in order to present a full picture of AMR’s drivers and impact. An intersectional approach to understanding gender and AMR can display how gender and other components ‘intersect’ to shape the experiences of individuals and groups affected by AMR. However, there are insufficient data on the burden of AMR disaggregated by gender and other socio-economic characteristics, and where available, it is fragmented. For example, to date, the best estimate of the global burden of bacterial AMR published in  Key messagesDifferent sexes and genders experience differentiated risks of acquiring bacterial infections, including drug-resistant infections, and of becoming ill. Different genders also have different health-seeking behaviours. Consequently, they are distinctly affected by antimicrobial resistance (AMR).Disaggregated data on the burden of AMR by gender and other characteristics are lacking and, where available, fragmented. Hence, perspectives on sex, gender and other socio-economic characteristics need to be incorporated into the study of both AMR and antimicrobial use (AMU).An intersectionality approach helps highlight how gender and other axes of marginalization that are common in low- and middle-income countries contribute to the exacerbated vulnerability to AMR.A more nuanced view of the burden of AMR on specific categories of the population as well as its drivers should inform not only any AMR interventions globally, including for national action plans, but also AMR surveillance, AMU monitoring, infection prevention and control and public and professional awareness measures, both donor and government funded.It is estimated that in 2019, 4.95 million deaths were associated with bacterial antimicrobial resistance (AMR), the highest death rate of which is in western and sub-Saharan Africa, with 27.3 deaths per 100 000 . AMR is The drivers of AMR are complex and multifaceted and differ from one place to another. In low- and middle-income countries (LMICs), evidence suggests that the main drivers of AMR include the misuse and overuse of antimicrobials; lack of access to clean water, sanitation and hygiene (WASH); limited resources for infection prevention and control (IPC) in healthcare facilities and farms; limited access to quality, affordable medicines, vaccines and diagnostics; lack of awareness and knowledge and lack of enforcement of legislation . The mosNowhere is the burden of AMR greater than in low-resource settings where weak health systems, including a lack of health information systems and limited resources, compromise AMR surveillance . Based oThe general economic impacts of the threats of AMR on global health are well understood. However, the impacts of AMR go beyond the global economy and have dire consequences and more pervasive social impacts, which can disproportionately affect certain groups compared with others. While an economic approach may be informative, it lacks the ability to put a ‘human face’ to the problem . Long anThe impacts of AMR are however not the same for everyone, even for people with shared socio-economic characteristics. Different sexes and genders experience differentiated risks of acquiring bacterial infections, including drug-resistant infections (DRIs), and of becoming ill. Different genders also have different health-seeking behaviours (HSBs) and differentiated access to health care and education that shape their likelihood of appropriately using and administering antimicrobials  are still missing. For example, to date, the best estimate of the global AMR burden , p. 1.This paper presents and discusses the result of a scoping review conducted to explore the body of literature that bridges sex and gender, on the one hand, and drivers and impact of AMR, on the other hand. In doing so, this paper makes the case for the need to incorporate intersectional gender perspectives into the study of both AMR and antimicrobial use (AMU). Sex- and gender-disaggregated data and an intersectional analysis of this can provide development actors and policymakers with a more nuanced view of the burden of AMR on specific categories of the population as well as its complex drivers to better tailor strategies and programmes aimed at reducing its emergence and spread while maximizing equity and social justice mandates. This would in turn contribute to achieving better and more equitable outcomes in reducing the emergence and spread of AMR globally. It is also central to the attainment of many sustainable development goals .In this paper, we understand ‘sex’ as the biological and chromosomal reality of one’s body, which categorizes male, female and intersex people , p. 11. 1 such as occupation, income, age, geographic location, marital status, education and disability in mutable and dynamic ways to exacerbate inequalities in access to information as well as quality health care by specific people in specific places  agencies’ reports included the UN iLibrary, the WHO Online Library and the WHO Institutional Repository for Information Sharing. The main search terms used included a combination of AMR-related terms, gender-related terms and topic-specific terms Table A1.This search retrieved peer-reviewed literature, including primary research, systematic, scoping and literature reviews, commentaries, author correspondence and opinion papers. These papers encompassed a variety of methods and approaches, from experimental and quasi-experimental designs and observational studies to qualitative studies using ethnography, feminist research, action research or grounded theory. This wide scope was justified by the limited literature that links AMR and gender and our aim to scope out the available literature across all fields.This search also included grey literature, which included reports published by UN bodies or agencies.The titles and abstracts (or table of contents and introductions for reports) of retrieved texts were initially screened according to their relevance to the two main research questions outlined earlier and subsequently screened according to our inclusion criteria . Limited access to safe water particularly impacts women and young girls as they generally fetch water for the household  and may Intimate relationships and sexual behaviours also highlight the intersectional nature of exposure and vulnerability to AMR, whereby biological characteristics of one’s sex intersect with one’s sexual orientation, gendered roles within a relationship, sexual violence and sex work .The rest of this section highlights the various ways in which sex, gender and their intersecting variables influence exposure to DROs and vulnerability to DRIs.Escherichia coli in their faeces that produce extended-spectrum beta-lactamase enzymes that provide resistance to antibiotics such as penicillins, cephalosporins, cephamycins and to some extent carbapenems , which have a high drug resistance potential, is increased in low-resource settings whereby WASH facilities are not readily available , including drug-resistant TB: a South African study highlights that extensively drug-resistant-TB and MDR-TB are six to seven times higher among healthcare workers than in the general population  among men who have sex with men (MSM) who are a particularly high-burden group . GonorrhSome studies show that men are more likely to engage in risky sexual behaviours (RSBs) than women, which may put them at a higher risk of contracting drug-resistant gonorrhoea (DRG) . These sN. gonorrhoea is more prevalent in sex workers in Ankara, Turkey  captures the ‘clusters of disadvantage’ , which iHSBs can drive exposure to AMR, particularly in settings where access to prescription-only drugs is unregulated. These behaviours are often not linear and are made complex by the plurality of health systems as well as the inter- and intra-household power dynamics that influence healthcare decision-making in many low-resource settings. Antimicrobial prescription, administration and self-medication are keys to understanding people’s access to antimicrobials and rational or irrational use. Overall, education and knowledge play a crucial role in compliance with antibiotic treatment, lower expectations of antibiotic prescriptions and lower rates of self-medication , which hStudies in LMICs have shown that the likelihood of prescribing or being prescribed antimicrobials is influenced by gender. Veterinary prescriptions for animal health seem to be influenced by the gender of a veterinarian. A study in Pakistan reveals that male veterinarians had higher odds of prescribing mass medication and use of antibiotics for disease than females, whereas improper disposal of expired antimicrobials was more common in female veterinarians  also influence HSBs that further compound the problem of AMR. For example, On the one hand, as shown in Uganda’s low-income regions, men tend to have higher rates of self-medication, due to greater purchasing power that enables them to obtain medication from the private sector, a more expensive option unavailable to women . Non-comOn the other hand, women as primary caregivers have a crucial impact on the medication of children and relatives they care for . MaternaWhere the mothers have work commitments, however, they are more likely to medicate their children without seeing a doctor or without a prescription to avoid impinging on time spent earning an income for the family  or sexual orientation may be difficult to collect due to discrimination, and unsafe for a patient to disclose, when accessing health care. Other data such as cultural practices may also not be possible to represent on an epidemiological level. Nevertheless, this is where anthropological approaches may come into the picture to allow a contextual approach of AMR, at the community level.This paper has shown how gendered lived experiences of AMR are different based on where and how one lives. We argue that ignoring how gender and other social stratifiers influence the burden of AMR masks people’s lived experience of health care and exposure to AMR. Studying AMR without considering how gendered intersections shape people’s lives lacks the precision to truly understand how to tackle the burden and spread of this silent and overlooked pandemic .Health systems are not gender neutral, and they are not always accessible, affordable and acceptable to all groups within the population , which iOur analysis of the literature sourced through our review has demonstrated the need for further research using an intersectional gender lens to unpack how sex, gender and other social stratifies interact in different contexts to drive both the burden and the spread of AMR. In order for such research to be possible, national AMR surveillance systems as well as AMU monitoring systems  and AMR burden estimations need to be designed so that they will be able to collect and collate data disaggregated by sex, gender, age, education, occupation, ethnicity, place of residence and other relevant social stratifiers. In addition to quantitative data, there is also a need for additional research taking a social science and anthropological approach to uncover the social underpinnings of AMR drivers.Building on both quantitative and qualitative evidence, we suggest that health researchers working on interventions to avert the burden of AMR should consider applying an intersectionality approach in order to better understand exposure to, vulnerability to and drivers of AMR for different people in different contexts. Similar approaches including One Health that foster interdisciplinarity should also be considered, including operational research on transmission routes of AMR between humans and between humans, animals and the environment within specific communities.This would have several benefits. First, the evidence generated could help practitioners  to improve treatment of infections within their communities, thereby contributing to lessening the burden of infections including DRIs and decreasing the need for using antimicrobials overall. Second, this analysis could and should be used to better tailor measures to prevent infection . Third, this evidence could inform initiatives to lower people’s vulnerability to becoming ill . Finally, this could also drive interventions to improve access to quality diagnostics and quality-assured treatment for all .Integrating an evidence-based intersectional gender lens in all policies and plans to tackle AMR, starting with national AMR action plans, will not only increase their chances to be effective at promoting rational use of antimicrobials for all but also lessen the burden of AMR . It would also help tackle the spread of AMR  and contribute to global-level goals such as the drive towards Universal Health Coverage, the Immunization 2030 agenda and ultimately the sustainable development goals with their promise to leave no one behind.


Antimicrobial resistance (AMR) is a challenge to modern medicine. Interventions have been applied worldwide to tackle AMR, but these actions are often not reported to peers or published, leading to important knowledge gaps about what actions are being taken. Understanding factors that influence the implementation of AMR interventions and what factors are relevant in low-middle-income countries (LMICs) and high-income countries (HICs) were the key objectives of this exploratory study, with the aim to identifying which priorities these contexts need.A questionnaire was used to explore context, characteristics, and success factors or obstacles to intervention success based on participant input. The context was analyzed using the AMR-Intervene framework, and success factors and obstacles to intervention success were identified using thematic analysis.Of the 77 interventions, 57 were implemented in HICs and 17 in LMICs. Interventions took place in the animal sector, followed by the human sector. Public organizations were mainly responsible for implementation and funding. Nine themes and 32 sub-themes emerged as important for intervention success. The themes most frequently reported were ‘behavior’, ‘capacity and resources’, ‘planning’, and ‘information’. Five sub-themes were key in all contexts , two were key in LMICs , and five were key in HICs .LMIC sub-themes showed that funding and surveillance were still key issues for interventions, while important HIC sub-themes were more specific and detailed, including mandatory enforcement, multiple profiles, and personnel needed for good management and good design. While behavior is often underrated when implementing AMR interventions, capacity and resources are usually considered, and LMICs can benefit from sub-themes captured in HICs if tailored to their contexts. The factors identified can improve the design, planning, implementation, and evaluation of interventions. ConsideImplemented AMR interventions have targeted many settings and scales with varying impacts due to the influence of the context in which they take place , 12. WhiPublished interventions are a major source of knowledge in implementation science, but sometimes they do not follow established reporting guidelines, and if they do, these guidelines are insufficient for capturing relevant details of the social-ecological system , 17. MorAlthough implementation science based on evidence takes time , explori2.A questionnaire was developed based on the AMR-Intervene framework to contextualize the social-ecological system , 12, andWe conducted a scan of potential participants who worked on AMR or in industries or settings that can be impacted by AMR  and could be knowledgeable about AMR interventions. Potential participants were identified through: (1) our consortium network; (2) public sources such as the World Health Organization repository National Action Plans on AMR; (3) web-based searches; and (4) official websites of governments, industries, and non-governmental organizations. Potential participants were classified based on the regions defined by the World Health Organization  and the potential sectors to ensure geographic and professional diversity. We used three different distribution methods: (1) potential participants identified were invited to participate in the study via email with a survey link, (2) distribution of the survey link through email via AMR networks such as ReACT, WorldFish, and STRAMA; and (3) survey available at the project website.Interventions were analyzed in their social context  and ecological context . The context of the social-ecological system was analyzed using the AMR-Intervene framework . For obs3.n = 57), almost 25% of interventions were reported in LMICs (n = 17), one was implemented locally in two countries (one HIC and the other LMIC), and two interventions had a global scope. Interventions were located in America (3 countries), Europe (7 countries), South-East Asia (2 countries), Africa (14 countries), globally, and in the West-Pacific Region (1 country) (n = 35), Sweden (n = 10), and India (n = 10) were the countries with the largest number of interventions reported. The sector in which most interventions were implemented was the animal sector, followed by the human sector (n = 43)) and without an end date (n = 56) (n = 21) had a mode duration of 3 years and an average duration of 4 years.This exploratory study collected data from 77 interventions and their contexts . The ecocountry) . Canada n sector . The old(n = 56) . Time-bon = 21) had no specific funding source (n = 27) or AMR prevalence (n = 17) or by their combination (n = 9). Thus, interventions were mostly reactive in response to a specific problem already happening (n = 71), while only a few were preventive (n = 6) (n = 55) and surveillance of AMR and/or AMU . At the level of implementation, almost half of them were implemented nationally (n = 38) (n = 27) reported specific bacteria or the yeast Candida auris (n = 25)  . The mai(n = 38) . Almost da auris . The mosda auris  at the s(n = 25) .Regarding intervention assessment, 21% were assessed, and of these, one-third reported positive outcomes—five reported decreased AMU and one decreased antibiotic resistance genes. Only three of the interventions assessed published their results in scientific journals. More than half of the interventions (51%) had the assessment in progress, and 27% did not consider the assessment when planning the intervention. However, subjectively, the majority of interventions were perceived to have positive outcomes. Overall, the cost-effectiveness of interventions was unassessed, but one intervention was highlighted as cost-effective. Another intervention  reported unintended consequences (outcomes that were not foreseen previously) that included annoyance and low self-esteem in some professional groups related to healthcare.When comparing HIC and LMIC contexts, the timeline of interventions reported was similar in both groups, with most implementation done in the last 5 years. The agents responsible were, in most cases, public institutions. The animal sector was the most targeted in both LMICs and HICs, and the proportions of sectors were also similar between these two contexts, as shown in Nine main themes and 32 sub-themes were captured in this exploratory thematic analysis . The mosThe same sub-themes were reported in both HICs and LMICs except for the following five sub-themes that were reported only in HICs and not in LMICs: flexibility and adaptability; affordability; preventive character; financial implications inside the theme AMU; and investment in novelty and research. Most sub-themes enhancing or hindering the success of interventions were reported both as success factors and obstacles. Nonetheless, nine sub-themes were only reported as success factors , and one sub-theme  was reported just as an obstacle to intervention success . Ten subp-value was as low as 0.0005, and the test for obstacles had a p-value of 0.043. About important sub-themes, only the test for reporting success factors had statistical significance , but not the test for obstacle reporting . None of the Fisher’s exact test p-values for each theme and expected sub-themes were statistically significant (p > 0.05), and they are reported in Fisher’s exact test to see if themes and sub-themes were reported independently of the HICs and LMICs context resulted in a value of p of 0.38 for all ‘themes’ and 0.18 for the most important ‘sub-themes’. If we look at how themes were reported, Fisher’s exact test for all themes showed statistical significance: for success factors of themes, the 4.Historically, tackling AMR has been dominated by strategies aimed at finding new antimicrobials and reducing the need for antimicrobials. However, the weak pipeline of research and lagging efforts for new antimicrobial drugs  have lefAMR responses targeting AMU rely on behavioral change to improving how we use antimicrobials and, if possible, reduce demand in multiple settings and at multiple scales. Therefore, it is fair that ‘behavior’ stood out as the most reported theme in this study, a finding that aligns with our previous study on HICs . BecausePredictably, the ‘capacity and resources’ of the system were the second key theme. ‘Funding and finances’ were identified as vital for the success of interventions, and worrisome is that (lack of) ‘personnel’ has been highlighted as an important sub-theme hampering chances of success due to shortages, time overloads, and untrained actors that can lead to risky behaviors and actions contributing to the spread of AMR , 27. In Probabilities of positive outcomes in AMR interventions also rely on careful ‘planning’, which was the third theme in importance, with good ‘implementation’, ‘assessment’, and ‘design’ sub-themes being key. Good planning considers the capacity of the system and resources available at the time of implementation, but it should also detail how behavioral change is impacted. The description must include which actors shall be involved; social, historical, economic, or political contextual factors that influence the behavior of interest; and the time and frequency needed to routinely accept and adapt the intervention to avoid the tendency to return to old habits , 28–30. Taking all themes together, we could see statistical differences in theme reports both in success factors and obstacles, meaning that there were differences in how they were reported. If we look at how the most important sub-themes were reported, only the reporting of success factors showed statistical differences between HIC and LMIC contexts. Statistical differences between each theme (and sub-themes) captured between HIC and LMIC contexts were not found. LMICs had ‘funding and finances’ and ‘surveillance, epidemiology, and preventive screening’ as key themes for success and did not have much detail about the issue other than that they are at early-stage phases that manifest that they are still developing these surveillance and epidemiological systems, while HICs had more factors of success that were more descriptive, specific, and detailed, such as ‘multiple profiles’, ‘mandatory enforcement’, ‘management’, or ‘design’. Therefore, themes and sub-themes leading to success seem to be similar independent on the context, but how they are perceived is different. LMICs would benefit from considering the sub-themes captured in HICs when developing AMR interventions for their contexts once they fulfill their basic needs for better resources, surveillance, and epidemiology.Most interventions reported in this study were part of the ‘gray’ literature, meaning they were not published in scientific journals. The context of both HIC and LMIC interventions was studied with the AMR-Intervene framework . The broReported interventions in our study were triggered by the state of AMR, or AMU, which is commonly recognized as a major driver of AMR and is accelerated by misuse and massive use . InterveInterventions in this analysis were implemented mainly in the animal sector . In contEscherichia coli, Salmonella sp., Staphylococcus aureus, Enterococcus sp., Campylobacter sp., or Klebsiella sp.), which can hamper global progress toward the Sustainable Development Goals that have a direct or indirect relationship with AMR  will likely not have enough leverage to address AMR. However, combining them may positively impact reducing AMR, emphasizing the use of several approaches to maximize success. The complexity of the problem demands wider approaches involving ecological and biological, as well as social and psychological sciences , 37 beca5.The main advantage of this analysis is that it compares factors contributing to the success of interventions according to the socio-economic context in which they take place: HICs and LMICs. It also aimed to involve a wide audience that is engaged in AMR mitigation, either directly or indirectly, even though we cannot be sure about how successful we have been. Non-traditional stakeholders are needed (but often not considered) in addition to traditional stakeholders to identify multi-pronged and sustainable perspectives to tackle and reduce AMR and its impacts on humans, animals, and the environment . This ex6.Our goal of studying an approximately similar number of human, animal, and environmental interventions evenly located in the different WHO regions was not met, even though participants from organizations worldwide were invited to participate. Of all WHO regions, we were not able to engage the Eastern Mediterranean Region. Important themes may be missing for this region, because not all sectors and types of interventions reported were equally represented. Important themes in LMICs may be missing as contexts can be highly heterogeneous compared to HICs, which have better integration and organization in their health and surveillance systems . Even if7.Perceived factors that are cornerstones for interventions to be successful were grouped into 9 themes and 32 sub-themes. To our knowledge, this exploratory approach is the first one aiming to engage a wide variety of stakeholders worldwide to cast light on factors that contribute to the success of interventions from different perspectives. Using this inclusive view and by applying a social-ecological lens, five key sub-themes emerged as universal in HICs and LMICs, while other sub-themes emphasized what must be considered differently in each. By capturing the experiences of interventions implemented in HICs whose basic needs and resources were covered, this study has helped to identify more detailed key factors for successful interventions. These identified factors can help strengthen policies and AMR intervention planning in LMICs as they can be applied and tailored to these resource-scarce settings. Building resilience toward AMR requires proactive approaches and novel insights from qualitative and behavioral sciences that are able to capture the heterogeneity and details that affect AMR.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The questionnaire followed the approved procedures by the University of Waterloo Research Ethics Committee and it was reviewed and received ethics clearance through a University of Waterloo Research Ethics Committee (ORE#40519).TG, IL, and MC: data collection and curation. TG: formal analysis. SM, DW, and PJ: funding acquisition. TG: methodology and writing—original draft. TG, IL, MC, AL, PH, MT, CC, EP, SM, DW, and PJ: writing—reviewing and editing. All authors contributed to the article and approved the submitted version.


Introduction: Drugs and biocompatible nanoparticles have raised significant potential in advancing the bone regeneration. Electrospinning technology enables the full realization of the value of drugs and nanoparticles.Methods: In this study, we have successfully fabricated core–sheath nanofibers solely composed of polycaprolactone (PCL) polymer. Simvastatin (SIM) was confined to the core of the nanofibers while nanohydroxyapatite (nHA) was loaded on the nanofiber surface.Results: All the prepared nanofibers exhibited a cylindrical micromorphology, and the core–sheath structure was exploited using a Transmission Electron Microscope. X-ray pattern results indicated that SIM was in an amorphous state within nanofibers, while Fourier Transform InfraRed spectroscopy showed excellent chemical compatibility among SIM, nHA, and PCL. The actual loading of nHA within the nanofiber was determined by a thermogravimetric test due to the high melting point of nHA. Core–sheath nanofibers could release SIM for 672 h, which was attributed to the core–sheath structure. Furthermore, nanofibers loaded with SIM or nHA had a positive impact on cell proliferation, with the core–sheath nanofibers displaying the most favorable cell proliferation behavior.Discussion: Such a synergistic facilitation strategy based on materials and nanostructure may encourage researchers to exploit new biomedical materials in future. Bone defects caused by external environmental stimuli remain a rapidly increasing trend, and the national healthcare expenditure is anticipated to substantially increase in the foreseeable future . AutologElectrospinning is a prevalent technology for top-down production of nanofibers, which utilizes high-voltage electrostatics to draw working fluids into filaments . During The sustained and efficient release of drugs is largely dependent on the properties of materials involved . MateriaIn this work, we have adeptly fabricated core–sheath nanofibers utilizing PCL as the exclusive polymeric material. SIM, an active pharmaceutical ingredient, has been incorporated in the core layer, while the sheath layer contains nHA. The detailed characterization and description of nanofibers are elaborated upon in this work.White PCL particles with the molecular weight of 80,000 g/mol was supplied by Sigma . Phosphate Buffered Saline (PBS), acetone, and hexafluoro-isopropanol (HFIP) was purchased from China National Medicines Corporation Ltd., . SIM was purchased from Aladdin . nHA was purchased from McLean . BS350A CCK-8 solution was supplied from Labgic Technology Co., Ltd , Primary human bone marrow derived mesenchymal stem cells (hBMSCs) and fetal bovine serum (FBS) were supplied from Sciencell , alpha minimum essential medium (α-MEM) and antibiotics were obtained from Thermo Fisher Scientific .Based on the only solvent HFIP, four polymeric solutions were prepared for single and coaxial electrospinning. 10 g PCL was used as the main polymeric, 0.5 g nHA and SIM were added into the polymeric solution to prepared suspensions and drug solutions. In coaxial electrospinning process, sheath and core solutions were prepared separately. The sheath solution was prepared by dissolving 7 g PCL and 0.5 g nHA in 100 mL HFIP, while the core solution was fabricated by dissolving 10 g PCL and SIM in 100 mL HFIP . SolutioThe aforementioned solutions were filled into 20 mL syringes via either 18 G needles or a coaxial spinneret. The spinneret was subsequently connected to high-voltage devices, operating within the range of 6–6.5 kV, by means of a clip. Then, the pumps were pushed to provide a constant rate for the syringes using a YFSP-T instrument . The nanofibers were collected using a drum collection device positioned at 16 cm from the spinneret. The flow rate of the three monolithic nanofibers was 1 mL/h; while for coaxial electrospinning, the flow rate of core and sheath was 1 mL/h and 1.5 mL/h. Then, nanofibers were placed in fume hood for 2 days to remove any residual solvent before being used for cell culture.The monolithic and coaxial nanofibers were subjected to spray aurum for 100 s in the presence of protective inert gas, to impart them with electrical conductivity. Thereafter, the micromorphology of these nanofibers was studied utilizing a scanning electron microscope . The average diameter of nanofibers was deter-mined by analyzing SEM pictures at least 70 individual nanofibers . The internal structure of fibers was confirmed by utilizing a Transmission electron microscope .–1.Physical forms of fibers were analyzed utilizing a Bruker–AXS X-ray diffractometer . It was carried out at 40 kV voltage and 30 mA current when the 2θ range was 10°–70°. Chemical compatibility between fiber components was analyzed by infrared Spectrometer . Fibers were directly placed on the stage and covered with a cover for observation at the range of 500–4,000 cm−1. In addition, the test was kept in an inert gas atmosphere.Accurately weighed 10 mg of samples were placed into an alumina crucible, and the TGA test was conducted by PerkinElmer Pyris 1 TGA analyzer  from 30°C to 600°C at the certain rate of 5°C minλmax = 238 nm utilizing UV-vis spectrophotometer . The drug loading efficiency (D) was measured using Eq. The SIM-loaded nanofibers were determined by PBS  as the release medium. 10.0 mg of nanofibers was placed into a plastic tube containing 45 mL PBS and kept in a shaking incubator . At specific intervals, precisely determined periods of time, 4 mL of PBS solution was extracted from the tube. Following this, an equivalent volume of fresh PBS was added. The experiment was performed in triplicate. The actual amount of SIM released was calculated at λmax = 238 nm. Three parallel experiments were carried out. The actual SIM amount was calculated by the linear regression equation .Nanofibers (10 mg) were completely dissolved in 4 mL acetone solvent for 2 h, and then diluted by PBS in 45 mL to obtain the absorbance of SIM at H) model is employed to determine whether the drug release followed dissolution release or diffusion release. And the Rigter–Peppas (P) model is utilized to investigate the release mechanism.The first-order (F) and zero-order (Z) kinetic models are conducted to analyze the relationship between the drug release rate and time. The Higuchi , t is the release time, and n is the drug diffusion coefficient.Where in vitro experiments. hBMSCs were cultured in α-MEM containing 1% (v/v) antibiotics and 10% (v/v) FBS at 37°C in an incubator .hBMSCs between three and five passages were used for the 4 per well were seeded. The culture medium was aspirated after incubation for 1 and 3 days, and cells were washed thrice with PBS. Plates were incubated at 37°C for 2 h after being supplemented with CCK-8 solution (100 μL each well). The absorbance of each well was determined by employing a Synergy HT microplate reader .The cell proliferation and biocompatibility of PCL scaffolds were analyzed. The PCL membrane was cropped into a 7 mm circular shape and put in a 24-well cell plate. After ultraviolet disinfection for 30 min, the PCL membrane was fixed with a sterile steel column. hBMSCs at a density of 1*10p < 0.05 means three is a significant difference between samples.Data were conducted at least thrice and processed as mean ± standard deviation. The One-way ANOVA Tukey’s test was performed on cell proliferation data using origin 2017 software, and The physical form of powders (SIM and nHA) and the prepared nanofibers was exhibited in –1 and 1,266 cm–1 were contributed to the C-O-C group, the peak at 1,695 cm–1 was contributed to the C=O group, the peaks at 2,871 cm–1 and 2,952 cm–1 were contributed to the stretching vibration of CH2 and CH3 groups, and the peak at 3,545 cm–1 was attributed to the stretching vibration of OH group  which suggests that the drug release in F4 nanofibers has both Fickian diffusion and matrix erosion mechanisms. The Higuchi and Peppas models are only suitable for analyzing monolithic materials with uniform drug distribution. However, since the core-sheath structure of F4 nanofibers increases the complication of the drug dissolution process, the drugs were not evenly distributed in F4 nanofibers. Therefore, the Higuchi model and Peppas model may not be suitable for analyzing drug release behavior in core-sheath F4 nanofibers. Nonetheless, the core–sheath structure of nanofibers not only can improve the drug release behavior, but also has a positive impact on the dissolution mechanism of drugs.Drug release behavior were further evaluated using several mathematical models in As shown in In this work, we successfully fabricated core–sheath nanofibers loaded with SIM and nHA simultaneously, resulting in nanofibers with a cylindrical morphology. The core–sheath structure and nHA nanoparticles were observed by TEM. The SIM existed in an amorphous form in nanofibers which had been confirmed by the XRD results, and the FTIR results indicated good chemical compatibility between the components of nanofibers. There was 3.45% nHA loaded in core–sheath nanofibers during the TGA analysis. The release time of SIM was controlled due to the protective effect of core–sheath structure, with a sustained release profile over 672 h. Cell proliferation tests showed that both SIM or nHA in nanofibers could promote cell proliferation, with the optimal behavior exhibited by the core–sheath F4 nanofibers loaded with both nHA and sustained-release SIM. The synergistic effect of the nanostructures and materials used in this study suggests the potential for promoting bone regeneration and may inspire further research in the development of other biomedical materials with similar synergistic effects.


Molecular motors are fascinating nanomachines. However, constructing smart materials from such functional molecules presents a severe challenge in material science. Here, we present a bottom‐up layer‐by‐layer assembly of oriented overcrowded‐alkene molecular motors forming a crystalline metal–organic framework thin film. While all stator parts of the overcrowded‐alkene motors are oriented perpendicular to the substrate, the rotors point into the pores, which are large enough allowing for the light‐induced molecular rotation. Taking advantage of the thin film's transparency, the motor rotation and its activation energy are determined by UV/Vis spectroscopy. As shown by gravimetric uptake experiments, molecular motors in crystalline porous materials are used, for the first time, to control the adsorption and diffusion properties of guest molecules in the pores, here, by switching with light between the (meta‐)stable states. The work demonstrates the potential of designed materials with molecular motors and indicates a path for the future development of smart materials. A regular array of oriented molecular motors is presented by incorporating overcrowded alkene molecules as linker in metal–organic framework thin films. By switching between the stable and metastable states, the adsorption and diffusion properties of the guest molecules are controlled. Inspired by molecular machines and molecular motors in natural biological cells, artificial molecular machines draw great scientific attention.Here, we present an oriented array of molecular motors. This material is based on oriented, highly crystalline SURMOF films with a pillared‐layer structure. The pillar moieties of the SURMOF structure, which are oriented perpendicular to the substrate, are the stators of the molecular overcrowded alkene motor. The rotor parts of the motor point into the pores and performs light‐fueled rotations Figure  and S1. The overcrowded alkene motor molecule, termed motor‐Py, is composed of a stator and a rotor part, Figure 2(BDC)2(motor‐Py) structure, see Figures 1a and S1.The X‐ray diffractograms  pictures, Figures The molecular isomerization resulting in the molecular rotation of the motor‐Py component in the SURMOF is characterized by UV/Vis spectroscopy, Figure The thermal helix inversion (THI) behavior of the motor inside the SURMOF was explored by measuring the transient UV/Vis spectra. In the transient spectra  and in the differential spectra , indicating that the THI‐relaxation is a first‐order reaction. At 25 °C, the  time constant is approximately 2300 s, corresponding to a half‐life time of ≈27 min. An Eyring plot of the temperature‐dependent time constants . A half‐life time of ≈12 min and a Gibbs free energy of 90.3±1.5 kJ mol reports.7, 18 Based on the time constant of the THI which is the rate limiting process for the rotation, we can determine the rotation frequency of the molecular motor in the SURMOF. Each full rotation comprises of two THI processes. While the full rotation of the rotor part takes about 1 h  at room temperature, the full rotation takes approximately 15 min at 70 °C.t=0, the pure nitrogen flow was switched to the nitrogen flow enriched with ethanol with an almost saturated vapor pressure.−2 to 1.12 μg cm−2, i.e. by about 19 %. Upon 470 nm irradiation, the uptake amount increases again. 3 cycles of 400 nm and 470 nm irradiation were performed, showing similar results, Figure 2 g−1, the calculated specific surface in the metastable state is only 3343 m2 g−1, i.e. 5 % smaller, see Supporting Information, table S1. The accessible pore volume per MOF unit cell also changes from 0.486 nm3 to from 0.429 nm3, i.e. by 12 %. This is consistent with the observed changes of the ethanol uptake.In the next step, we take advantage of the photo‐induced isomerization of the motor‐Py molecule in the SURMOF. To this end, the SURMOF was prepared on gold‐coated QCM sensors and the transient uptake of a probe molecule, here ethanol, was gravimetrically determined.nd law for a thin homogeneous film.[−14 m2 s−1, where the diffusion coefficient in the metastable sample is 2.1×10−14 m2 s−1, this means 40 % larger. This switching of the diffusion coefficient is reversible and was repeated for 3 cycles.In the plots of the transient uptake Figures a and S6,ous film.19, 21τ) determined by UV/Vis spectroscopy at 50 °C .In addition, we performed uptake experiments during the THI relaxation, this means during the rotation process, Figure In conclusion, a regular array of molecular motors is presented. The array is based on crystalline, nanoporous films of surface‐mounted MOFs with pyridyl‐functionalized overcrowded alkene motors as MOF pillars, standing perpendicular on the substrate surface. The high transparency of the homogeneous MOF film enables the exploration of the isomerization behavior by UV/Vis spectroscopy in transmission mode. There, the time constants of the molecular rotation and the Gibbs free activation energy for the rotation were determined. For the first time, a molecular motor in a nanoporous solid material is used to demonstrate a function. We showed that the uptake capacity and the mass transfer rate, i.e. the diffusion, can be controlled by light. Both properties are modified by switching between the stable and the metastable states of the motor moiety in the SURMOF.We foresee that such regular and oriented arrays of molecular motors will allow to realize further smart materials with functions like controlled membrane permeation of molecules or ions. We also believe that such SURMOF films can be used as well‐defined model system for advanced spectroscopy, giving insights in the quantum yieldThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.