Optimality models have a checkered history in evolutionary biology. While optimality models have been successful in providing valuable insight into the evolution of a wide variety of biological traits, a common objection is that optimality models are overly simplistic and ignore organismal genetics. We revisit evolutionary optimization in the context of a major bacteriophage life history trait, lysis time. Lysis time refers to the period spanning phage infection of a host cell and its lysis, whereupon phage progenies are released. Lysis time, therefore, directly determines phage fecundity assuming progeny assembly does not exhaust host resources prior to lysis. Noting that previous tests of lysis time optimality rely on batch culture, we implemented a quasi-continuous culture system to observe productivity of a panel of isogenic phage λ genotypes differing in lysis time. We report that under our experimental conditions, λ phage productivity is maximized around optimal lysis times ranging from 60 to 100 min, and λ wildtype strain falls within this range. It would appear that natural selection on phage λ lysis time uncovered a set of genetic solutions that optimized progeny production in its ecological milieu relative to alternative genotypes. We discuss this finding in light of recent results that lysis time variation is also minimized in the strains with lysis times closer to the λ wild-type strain. Evolutionary biologists have long been interested in the power of natural selection to refine biological adaptations –4. Gener–7–10–Because of their relative simplicity and amenability to experimental evolution, microorganisms, including bacteriophages (phages), have been popular model organisms to test predictions regarding optimality theory . For exa17–27–However, despite clear predictions, experimental tests using phages have generally failed to confirm theoretical predictions. Phages T7, ST-1, and ΦX174 with theoretically predicted suboptimal lysis times generally failed to evolve predicted optimal lysis times , 29. Whi17–However, we note that these studies used batch cultures to examine host-phage interactions, and phage fitnesses were estimated using absolute growth rates. These conditions may not effectively mimic the kinds of conditions experienced by phages in their natural habitats. To revisit the question of phage optimal lysis time, we employed a different approach, which was inspired by Bonachela et al. , who modLysis is the last step in the infectious cycle of lytic bacteriophage, resulting in the release of virions. Lysis timing in many phages is controlled by a single protein, holin, which accumulates in the cell membrane up to a genetically determined time, when it nucleates generating membrane-permeabilizing holes , 31. TheEscherichia coli, under quasi-steady state conditions. To initiate experiments, λ holin mutants differing in their lysis times  if exponential growth conditions are maintained  and the concentration of key macromolecules such as proteins, RNA, DNA, etc., increase exponentially with precisely the same doubling time  and starting host density (≈107 CFU/mL) at every transfer. Reaching a density of 107 PFU/mL after the first transfer is critical to maintain the productivity at 107 PFU/mL till the end of the fifth transfer. This is because a 1,000-fold dilution used to start the second transfer lowers the phage titer to the density that was used to initiate the first transfer (104 PFU/mL). If dilutions are accurately performed, the strains that reach a critical threshold of 107 PFU/mL can maintain this density throughout the transfer. After each transfer, the phage strains with lysis times between 60 to 100 min show increasing titers, which begin to plateau by the fifth transfer  were obtained after every transfer . Fig. 2 transfer . This al 122 min . InteresE. coli and phage λ in the animal gut milieu. The objective of this study was to elucidate short term ecological dynamics that may be predictive of long-term evolutionary dynamics. However, such studies may not necessarily provide insights into evolutionary dynamics due to genetic constraints that may play important but hitherto unknown roles. This was observed in a study where optimal foraging by bacteriophage ΦX174 could predict the ecology but not the evolution of host specialization  tend to select for strains with short lysis times  would shift to higher values when compared to the MOI values of strains with lower productivities  broth (5 g NaCl and 10 g tryptone in 1 L water) plus 0.2% maltose at 37°C. The overnight culture was diluted with equal volume of TB + maltose and grown for another 1.5 h. Then, 100 μL of these cells were mixed with appropriately diluted phage lysates and incubated at room temperature for 20 min to allow pre-adsorption. This mixture was then added to 2.5 mL of molten H-top agar  of lysogens was chemically fixed to a 22-mm square glass coverslip, which was pretreated with 0.01% poly-L-lysine  at room temperature for 10 min. Using this coverslip, a perfusion chamber  was assembled and immediately placed on a heated platform . The heated platform was mounted on an inverted microscope stage , and the perfusion chamber was infused with heated LB at 30°C . The chamber temperature was elevated to 42°C for 20 min and thereafter maintained at 37°C until ~95% cells were lysed. Videos of cell lysis were recorded using an eyepiece camera . The lysis times of individual cells were visually determined using VLC media player. Lysis time was defined as the time required for a cell to disappear after the temperature was increased to 42°C. For each lysogen, an average of approximately 100 cells was used to calculate the lysis time.To study the effect of lysis time on maintaining phage populations that undergo constant dilution, we employed a panel of λ phages harboring mutations in the holin gene n mutant . The celn mutant . BrieflyE. coli (~107 CFU/mL) culture growing exponentially in LB at 37°C in a shaking incubator (200 rpm) was infected with a phage lysate (~104 PFU/mL). The infection could proceed for 3 h, after which phages were separated by filtration using a 0.2 μ syringe filter . The filtered lysate was diluted 1,000-fold to start the next transfer using freshly growing cells (~107 CFU/mL). These steps were repeated four more times for a total of five serial transfers (We used wild-type λ productivity as a benchmark for comparisons with the holin mutants. To this end, we performed pilot studies by initiating wild-type λ infections of host cells. Phage λ is known to lyse most infected cells in an exponentially growing culture with a small proportion of cells producing lysogens . Howeverransfers . Plaque


Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works. However, vanilla GNN encoders ignore chemical structural information and functions implied in molecular motifs, and obtaining the graph-level representation via the READOUT function hinders the interaction of graph and node representations. In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol), which introduces a pre-training framework to learn molecule representation for property prediction. First, we present a Hierarchical Molecular Graph Neural Network (HMGNN), which encodes motif structure and extracts node-motif-graph hierarchical molecular representations. Then, we introduce Multi-level Self-supervised Pre-training (MSP), in which corresponding multi-level generative and predictive tasks are designed as self-supervised signals of HiMol model. Finally, superior molecular property prediction results on both classification and regression tasks demonstrate the effectiveness of HiMol. Moreover, the visualization performance in the downstream dataset shows that the molecule representations learned by HiMol can capture chemical semantic information and properties. Graph Neural Networks are employed to encode molecular graph representations, but structural information and chemical functions are largely missing. Here, the authors develop hierarchical molecular graph self-supervised learning as a framework to learn molecule representation for property prediction. Motivated by the remarkable success of machine learning2, especially deep learning3, molecular representation learning has increasingly explored machine learning-based methods. These methods are widely used for various molecular applications, such as chemical property prediction5, drug molecule generation8, and optimization9. How to learn comprehensive and effective molecular representation remains an open challenge.In recent years, molecular representation learning has attracted growing attention in the study of chemical drug analysis and discovery12, a range of methods apply language models to handle string-based molecular representations like SMILES13 and SELFIES14. Specifically, these molecular strings are encoded by Recurrent Neural Network (like Gated Recurrent Unit (GRU)15 and Long Short-Term Memory (LSTM)16 or Transformer12, which can be trained in a supervised manner by predicting molecular properties17. Further, to handle enormous unlabeled data, many works pay attention to self-supervised learning (SSL) frameworks. The SSL decoders generally generate the original strings18 or other random SMILES of input molecules19 or recover masked tokens of molecular strings20. Due to the single dimension of strings, important topology structures of molecular graphs are ignored. Therefore, many researchers have transformed their interests from 1D molecular strings to 2D graphs22. In view of the success of SSL, graph SSL-based pre-training frameworks27 have been increasing rapidly in the past few years. They capture the topology of 2D graphs and have also shown a positive effect in molecular analysis tasks. However, their commonality for all kinds of graphs leads to the neglect of unique structural properties of chemical molecules, such as rings and functional groups.Inspired by the development of Natural Language Processing (NLP)33 consider the characteristic of molecular graphs. Zhang et al.32 model a clustering problem to learn molecular motifs, and the GNN-encoded atom representations are grouped into subgraphs for contrastive learning. Zhang et al.30 design self-supervised molecular pre-training framework, whose pretext task is predicting the motifs based on a given order (depth-first search or breadth-first search) on the graph. Wang et al.5 present three molecular graph structural augmentation patterns and aligns different augmentations of the same molecule through contrastive learning. Wang et al.33 also propose a contrastive learning framework for molecular learning, in which not only molecular pairs but also motif pairs are sampled for contrastive pre-training. Wang et al.31 takes into account the chemical reaction relationships between the molecules, i.e., the sum of molecular representations of all reactants is supposed to equal that of all products. Despite the improvement of molecule representation learning, previous works still fail to solve the following problems and challenges: (1) How to preserve and capture molecular structure adequately? Many recent molecular learning methods28 apply graph augment to construct different views and contrast multiple views for pre-training. However, some general graph data augmentations (like edge modification34 or graph diffusion35) tend to destroy the structure or attributes of molecules, so some important chemical properties are highly possible to be buried. In addition to the whole topology structure, the motif is also valuable and has an important impact on the molecular properties. Therefore, it is beneficial and challenging for molecular learning to preserve the complete molecular structure and directly incorporate the motifs. (2) How to fuse more comprehensive information into molecular graph representations? With the development of GNN40, growing molecular learning methods31 leverage GNN as the encoder backbone, in which nodes flow information among local neighbors, and the graph representation is obtained by integrating all its node representations via READOUT function. The pattern of aggregating neighborhood representations lacks a global scale. Moreover, the READOUT operation can only transfer information from low-order atoms to the high-order molecule, but can not implement the interaction between them. (3) How to design the pretext tasks of self-supervised pre-training? Self-supervised pre-training requires design pretext tasks to optimize the backbone model parameters. The pretext tasks affect the transferable ability of the pre-training model to a great extent, which determines whether the model can achieve satisfactory fine-tuning performance in downstream tasks. Therefore, it is key for self-supervised pre-training models to design reasonable pretext tasks to improve transferable ability.Many recent presentation learning worksTo resolve the aforementioned challenges, we propose a self-supervised learning framework for molecular property prediction called Hierarchical Molecular Graph Self-supervised Learning (HiMol). Figure HiMol builds a hierarchical GNN to encode molecules. It facilitates incorporating multi-order information into the molecule representation. To the best of our knowledge, HiMol is the first molecular representation learning method that encodes node-motif-graph hierarchical information.HiMol constructs motifs based on chemical rules and mine the characteristics of molecular substructures without destroying chemical structures.HiMol augments a graph-level node to simulate the molecular graph representation. It allows the molecular graph representation to participate in the training directly and realizes bidirectional transmission of local and global features.HiMol designs multi-level self-supervised pre-training tasks in accordance with hierarchical representations of molecules. It improves the transferable potential of the pre-training framework and helps learn more informative molecular representations.HiMol outperforms the state-of-the-arts (SOTA) in the downstream molecular property prediction tasks including classification and regression. It demonstrates the effectiveness of our proposed HiMol.In summary, the advantages of our HiMol are as follows:Our HiMol presents a self-supervised pre-training framework to encode hierarchical representations of the molecular graph. HiMol mainly includes the Hierarchical Molecular Graph Neural network (HMGNN) and Multi-level Self-supervised Pre-training (MSP).41, where V and E denote atom and bond sets respectively. Then we decompose G into a series of motifs k is the number of motifs. The decomposition of motifs adds a rule on the basis of BRICS42, i.e., break large ring fragments and select their minimum rings as generated motifs. All motif nodes Vm are incorporated into V, and edges between the corresponding atoms and motifs Em are linked and merged into E. In addition, we augment a graph-level node Vg, meanwhile, edges between all motifs and Vg are appended into the initial graph G. The augmented graph Given the SMILES of a molecule, we first transform it into a graph As for the fine-tuning for downstream tasks, the graph-level representations pass through a 2-layer Multi-Layer Perceptron (MLP) to predict molecular properties. The pre-trained GNN weights are transferred to the fine-tuning model and continue to be updated along with the MLP parameters under the supervision of labels. More details about HiMol method are shown in “Methods”.To evaluate the effectiveness of HiMol for molecular property prediction, we conduct classification and regression tasks on diverse datasets from MoleculeNet. Table 28 on QM9 is not given since it is expensive to calculate the similarity matrix in the process of pre-training on QM9, and no corresponding result is given in the official paper. (1) HiMol outperforms all baselines on five out of six datasets and reaches competitive performance on the remaining QM8. Notably, the MAE decreases by 55.5% over the strongest baseline on the challenging dataset QM9. (2) Similar to the results of classification tasks, the LARGE version has a small edge over the SMALL version in the regression tasks.The regression results are shown in Table 43 to project them to a two-dimensional space and different colors to distinguish molecular property labels. Note that we simply visualize the fine-tuned molecular presentations in the test set, which do not incorporate groundtruth. From Fig. To exhibit intuitively the learned representations by HiMol, we utilize t-SNETo further demonstrate the effectiveness of our pre-training framework, we visualize the molecular representations fine-tuned in downstream tasks without pre-training in Supplementary Fig. q and the candidate molecule c is calculated as follows:To further investigate the semantic information contained in the pre-trained molecular representations via HiMol, we evaluate the similarity between the query molecule and other molecules and draw the top-five most similar molecules. The cosine similarity between the query molecule 38, which are directly trained through supervised downstream tasks. Table To investigate the impact of different fine-tuning strategies on the model prediction tasks, we explore freezing mode and full fine-tuning mode. They both inherit the pre-trained weights of the hierarchical molecular graph neural network. The freezing mode freezes the parameters of HMGNN and only updates the weights of downstream classifiers, while the full fine-tuning mode trains all weights of HMGNN and classifiers in an end-to-end manner during fine-tuning. In addition, we evaluate HiMol without pre-training and vanilla GINFigure To analyze the effectiveness of each part of our proposed HiMol framework, we conduct ablation experiments on HMGNN and MSP during the pre-training process. Figure In Fig. α simply sums all the loss values, removing the α weight parameter to balance. It can be observed that the combination of multi-level self-supervised tasks promotes the comprehensiveness transfer ability of pre-training compared with a single pretext task. In addition, the learnable weight α adjusts the importance of the loss of different parts, which can better coordinate the relationship between multi-level tasks to improve the performance of pre-training.The ablation versions for MSP are shown in Fig. 30 considering motif structure. They generally decompose molecules in a large-scale database into motifs and build a motif dictionary, then motif prediction serves as self-supervised pretext tasks in the pre-training process of molecular representation learning. However, these methods have some disadvantages: (1) When the constructed motif dictionary is large, the computation of motif prediction is expensive. (2) Motifs as the self-supervised signal only, are difficult to be incorporated by hidden representations directly. We encode motifs contained in each molecule in the encoder part, which directly integrates the motif structure into the hidden representations. Moreover, the augmentation of motif-level nodes better captures the affiliation relationship between atoms and motifs.First of all, we propose to encode the constructed motifs while learning the atom representations. HiMol mines the substructure of molecules through motifs. There are many existing molecular representation learning methods44 proposes to attach a token to the sequence, whose representation is regarded as the sequence-level feature. Referring to BERT, Graphormer45 adds a virtual node into the graph, which is linked and updated with all nodes, and the feature of the virtual node is the graph representation. Inspired by the success of the aforementioned works, we augment a graph-level node to extract the molecular graph representation via participating in aggregation and updating with all atoms. Nevertheless, related methods only connect the graph-level node with normal nodes, they are not conducive to capturing subgraph structure. To integrate motif nodes, we connect graph-level nodes with motif nodes to achieve hierarchical aggregation. Compared with the READOUT, our HiMol implements the training and updating of graph nodes and realizes the interaction with multi-order nodes after incorporating molecular motif structure.In addition to motif-level nodes, we also augment a graph-level node into the molecular graph, which participates in the aggregation process to extract molecule representation. Most molecular learning methods use vanilla GNNs as the backbones and squeeze node representations into graph representation through the READOUT function, i.e., graph pooling methods like MAX, SUM, and MEAN. The process of GNNs is introduced in Supplementary Note In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol) to learn molecular presentations. Specifically, we design Hierarchical Molecular Graph Neural Network (HMGNN) backbone to encode node-motif-graph hierarchical representations, which mines chemical semantic information implied at motifs and realizes the interaction of multi-order features. Furthermore, we build multi-level molecular-dependent tasks as self-supervised signals, capturing more comprehensive information from multiple perspectives. Extensive molecular property prediction experiments demonstrate that HiMol shows great improvements over SOTA methods.The superior performance of molecular representations reflects the potential of our HiMol framework, and there are interesting and promising future works on the basis of HiMol. (1) Combining molecular features with different dimensions, in addition to 2D graphs. (2) Simulating the generative process of molecules and extending our model to molecule generation and optimization tasks.We propose a Hierarchical Molecular Graph Neural network (HMGNN) to encode and represent molecular graphs, which mainly contains three parts: (1) motif construction; (2) augmented graph construction; (3) hierarchical representation encoder.First, we decompose each input molecule graph into a group of motifs. BRICS algorithm is a traditional approach to fragment molecule graphs, which designs chemical rules to break bonds. Specifically, BRICS structurally decomposes molecules into multiple functional fragments according to whether bonds can be combined. However, BRICS splits molecules based on limited chemical rules, resulting in coarse granularity of decomposition. After BRICS decomposition, some large motifs still contain a few more general and functional substructures to be partitioned. Therefore, we add a decomposition rule on the basis of BRICS, i.e., decompose more than one ring substructures connected by common chemical bonds into minimum rings. Figure Vm to the molecular graph. Naturally, node-motif edges Em are added between each motif node and all the atom nodes it covers. Since the final output is the molecular graph representation, we construct a graph-level node Vg into the molecular graph and link it to all motif nodes to form motif-graph edges Eg. The final augmented graph is represented as:All obtained motifs are added as nodes 38 is the most widely used to encode molecular graph representation. Inspired by this, we apply GIN as the backbone model of our HiMol, the aggregation pattern of lth layer is given as follows:l-layer, respectively, uv, and v. The detailed input features of atoms and bonds are shown in Supplementary Table On the constructed graph mentclass2pt{minimAfter encoding the hierarchical representations through HMGNN, we design generative and predictive pretext tasks to perform Multi-level Self-supervised Pre-training (MSP). The types of self-supervised learning corresponding to different pretext tasks are described in Supplementary Note yij represents the link between vi and vj, yv,k = 1 represents the atom type of node v is k, and ye,k = 1 represents the bond type of edge e is k. Their corresponding Katom and Kbond are the number of atom types and bond types.For atom-level representations, we design three generative tasks including bond links, atom types, and bond types, aiming at reconstructing graph structure and attributes. Specifically, three 2-layer MLPs with ReLU activation function project atom-level representations to predict three generative tasks, respectively. Cross entropy loss is employed to optimize the atom-level representations as follows:ya and yb are the number of atoms and bonds, respectively; H to the predicted values y. Finally, we add a learnable vector weight For graph-level representations, two predictive tasks on the overall properties of molecules are performed, i.e., the number of atoms and bonds. Similarly, two respective 2-layer MLPs with softplus activation function are applied to predict the graph-level tasks. Based on SmoothL1 loss, the loss of predictive tasks are given in Equations  and 8)..8).7\doc46 dataset. To evaluate the effectiveness of HiMol, we conduct molecular property prediction experiments on 12 datasets from MoleculeNet47, including six classification task datasets and six regression task datasets. All downstream datasets are split into 80/10/10% for train/validation/test through scaffold-split, which is split in accordance with the molecular structures and provides more challenge for prediction tasks than random-split. The statistical data of all datasets are summarized in Supplementary Table For the pre-training of HiMol, we utilize sampled 250K unlabeled molecules from the ZINC1539, GPT_GNN48, AttributeMask49, ContextPred49, InfoGraph50, GraphLoG26, GraphCL25, and JOAO51. Furthermore, we compare several methods designed specifically for molecular graphs, i.e., MoCL28, MolCLR5, G_Motif29, and MGSSL30. The detailed description of baselines is demonstrated in Supplementary Note To evaluate the effectiveness of our proposed HiMol, different types of self-supervised learning SOTA are conducted as benchmarks. We compare diverse universal graph learning methods, including GraphSAGEAll experiments are conducted on the Linux server with Nvidia GeForce GTX 1080 Ti GPU and Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz. In the pre-training phase, the HiMol run 100 epochs using the Adam optimizer with a learning rate of 0.001. We train the LARGE and SMALL versions, with 3-layer and 5-layer GIN as the HMGNN backbone. The dropout rate for the GIN backbone is set to 0.5. The batch size is set as 32 and the number of workers for dataloader is 8. The hidden representations of all levels are 512-dimension.In the fine-tuning phrase, we implement a 2-layer MLP with ELU activation function as the classifier. The weights of both pre-trained HMGNN and the classifier are optimized by the Adam optimizer with respective learning rates ranging from 1e-4 to 1e-3. For each downstream task, we fine-tune for 100 epochs and report the average test results of five runs. The batch size is 32 and the number of workers is 4. The embedding dimension is 512, the same as that in the pre-training phase. The dropout rate is adjusted in the range . The parameter settings for each dataset during the fine-tuning process are reported in Supplementary Table 28. MoCL does not implement transfer learning, and it is computationally expensive to calculate the similarity matrix of ZINC15 dataset during pre-training. Therefore, we adopt the same manner as the official work: pre-training on each downstream dataset and then fine-tuning on the same dataset for molecular property prediction tasks. All baselines maintain the same split proportion and manner on the downstream datasets.For the purpose of fairness, we pre-train all the baselines on the same dataset ZINC15 according to their official codes, except MoCLPeer Review FileSupplementary Information


Antimicrobial resistance (AMR) circulates through humans, animals, and the environments, requiring a One Health approach. Recently, urban sewage has increasingly been suggested as a hotspot for AMR even in high-income countries (HICs), where the water sanitation and hygiene infrastructure are well-developed. To understand the current status of AMR in wastewater in a HIC, we reviewed the epidemiological studies on AMR in the sewage environment in Japan from the published literature. Our review showed that a wide variety of clinically important antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARGs), and antimicrobial residues are present in human wastewater in Japan. Their concentrations are lower than in low- and middle-income countries (LMICs) and are further reduced by sewage treatment plants (STPs) before discharge. Nevertheless, the remaining ARB and ARGs could be an important source of AMR contamination in river water. Furthermore, hospital effluence may be an important reservoir of clinically important ARB. The high concentration of antimicrobial agents commonly prescribed in Japan may contribute to the selection and dissemination of AMR within wastewater. Our review shows the importance of both monitoring for AMR and antimicrobials in human wastewater and efforts to reduce their contamination load in wastewater. Antimicrobial resistance (AMR), which makes common life-threatening infections difficult or impossible to treat, is continuing to increase globally and now has significant public health and socioeconomic implications. According to recent estimates, if not tackled by 2050, AMR could cause 10 million deaths annually and global economic losses of up 100 trillion USD . TherefoIn terms of One Health, wastewater is an important reservoir of AMR because it contains various AMR from humans, animals, and environments . The resAlthough the direct discharge of untreated human waste containing many ARB and ARGs into the soil and water environment is one of the major contributors to environmental AMR contamination in LMICs ,12, in HIn Japan, the population coverage of wastewater treatment systems is 91.4%, which is one of the highest rates in the world ; however3 [As in other HICs, in Japan, most of the large centralized STPs use a conventional activated sludge (CAS) process, and most of the small to medium-sized STPs use an oxidation ditches (OD) process, usually combined with chlorination ,23,24,253 ; howeverTo understand the current knowledge about and challenges related to AMR in wastewater in a HIC, we performed a literature review to evaluate the current status of research on AMR in wastewater in Japan. On the basis of the review, we further assessed the risks to humans of AMR and antimicrobials in sewage on humans and the effectiveness of risk mitigation interventions in HICs.For this review, we focused on AMR in bacteria other than acid-fast bacteria. We excluded articles on non-bacterial microorganisms, including fungi and viruses, and did not consider disinfectant resistance. Although agricultural wastewater and industrial discharge are also considered to be important reservoirs of AMR in other countries , our revhttps://pubmed.ncbi.nlm.nih.gov/, accessed on 19 May 2022) and the Igaku Chuo Zashi (ICHUSHI) database, a database of articles written in Japanese , with the following keywords: Japan, antimicrobial resistance, wastewater, and sewage  by polymerase chain reaction (PCR) [tetA, tetB) [The characteristics of the included articles are summarized in  species ,36,37,38 species  and meta species  to direc species ,43,44,45on (PCR) ,34,37,39A, tetB) ,34,39. IOf the 11 articles on antimicrobials, 10 (90%) described studies that used liquid chromatography-mass spectrometry (LC-MS) to determine the concentration of antimicrobial residues in water samples ,52,53,54Klebsiella quasipneumoniae subsp. quasipneumoniae in urban STP influent; the bacterium harbors IncA/C2 plasmid and carries the carbapenemase gene blaKHM-1. Tanaka et al. [E. coli strains detected in STP influent and showed that clinically relevant strains belonging to global epidemic clone B2-ST131, which carry global dominant ESBL genes such as blaCTX-M-14, blaCTX-M-15, and blaCTX-M-27, were widely distributed in sewage in Japan. Azuma et al. [Enterobecteriaceae (CRE), multidrug-resistant Pseudomonas aeruginosa (MDRP), multi-drug-resistant Acinetobacter (MDRA), methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE), in STP influent. Azuma et al. also showed that the concentrations of ARB were 206 to 653 colony-forming units per mL (CFU/mL) for CRE, 825 to 1923 CFU/mL for ESBL, 174 to 1829 CFU/mL for MDRA, 41 to 978 CFU/mL for MDRP, 35 to 892 CFU/mL for MRSA, and 34 to 916 CFU/mL for VRE and that these concentrations were 1/10 to 1/1,000,000 lower than those in LMICs [Suzuki et al.  detecteda et al.  analyzeda et al.  and Eda a et al.  detectedin LMICs ,63,64,65in LMICs .Azuma et al. ,46 reporE. coli strains isolated from river water before and after the confluence of STP effluent; the group found that the concentration of E. coli and the percentage of strains that were resistant to one or more of the antimicrobials was higher in the effluent than in the river water but that the concentration and the percentage in the river water increased after the confluence of the effluent. Similar to the above studies, Iwane et al. [E. coli strains resistant to one or more of ampicillin, cephalothin, kanamycin, gentamicin, tetracycline, chloramphenicol, and nalidixic acid in STP effluent and river water. Nishiyama et al. [vanC genes. These articles suggest that various ARBs are present in STP effluent in Japan and that they represent a loading source of ARB in river water.It is unclear whether such a low concentration of ARB could affect the water quality of rivers, i.e., whether it would make them a source of AMR. Yamashita et al.  investige et al.  reporteda et al.  reportedvanA and vanB in water samples collected at each stage of the CAS and chlorination processes and in STP effluent, and Liu et al. [blaTEM, tetA, tetW, sul1, ereA, and qnrD in river water. Suzuki et al. [E. coli strains resistant to one or more antimicrobials, including ampicillin, cefazolin, cefotaxime, and ciprofloxacin, were abundant in the periphyton and sediment and that the drug-resistant E. coli strains were genetically close to other non-resistant E. coli strains detected in the same periphyton and sediment. These reports showed that ARGs in sewage are not degraded by STP treatment and could be an important source of ARGs in river water [Furukawa et al.  detectedu et al.  showed ti et al.  examineder water ,56.E. coli and MRSA with the molecular characteristics of hospital-acquired MRSA [AS, which is produced during the wastewater treatment process and used to treat wastewater, contains a dense microbial community and is known to be an important reservoir that maintains STP levels of ARB and ARGs, including multidrug-resistant fecal coliforms such as ESBL-producing red MRSA ,67,68.Acidovorax sp.) [E. coli sampled at each stage of the AS process  at an STP. Among those stages, the AS and return sludge had the highest abundance of drug-resistant E. coli strains, and the abundance of such strains was higher in secondary treatment effluent than in influent, although the total population of E. coli was lower. Suwa et al. [E. coli strains in secondary treatment effluent after the AS process. These reports suggested that AS may serve as a reservoir of ARB in STP, but few studies examined ARB other than E. coli or ARGs in AS. Further research on ARB and ARGs is needed to clarify how AS affects AMR in STP in Japan.We found only a limited number of articles on studies that examined AMR in AS in Japan. Residual ARB and ARGs in AS were reported by Miura et al. (β-lactam-antibiotic-resistant rax sp.)  and Morirax sp.) . Honda erax sp.)  evaluatea et al.  reportedIn both HICs and LMICs, ARB and ARGs generally are more abundant in hospital effluent than in community effluent, although the volume of hospital effluent is quite lower than that of urban communities ,16. One E. coli than river water and general wastewater do. These reports showed that hospital effluent is also an important source of clinically relevant ARB in Japan. However, our literature search found no articles on the correlation between the ARB that cause infections in hospitals, and the ARB detected in wastewater, so the direct risk of ARB in hospital effluent to humans or environments remains obscure.In Japan, Azuma et al. ,46 and EAzuma et al. , YasojimAzuma et al.  and NakaIn contrast, investigations of nationwide river water in Japan by Azuma et al.  and MuraSalmonella spp. strains resistant to multiple drugs, including ampicillin, third-generation cephems, aminoglycosides, and tetracyclines, detected in STP influent had high genetic homology to clinical isolates from human feces. Gomi et al. [E. coli clone ST131 C1-M27 was commonly found in both hospital wastewater and STP effluent. Since ARB derived from humans are the best candidates for transmitting AMR back to humans, these articles showed that exposure to wastewater carries a significant risk of infection by those ARB.Knowledge of the links between AMR in wastewater and in people in Japan is still scarce. Using molecular phylogenetic analysis, Ishiguro et al.  and Yanai et al.  showed tE. coli, K. pneumoniae, and Aeromonas spp. strains in coastal waters of Tokyo Bay, Japan [The main sources of AMR and antimicrobials are STP effluent discharged into rivers and seas and treated sewage sludge applied to agricultural land ,18. Humay, Japan ,41,42. Ty, Japan .Most of the examined articles in this review reported on studies that assessed AMR in wastewater by culture-based methods, which are well-formulated and standardized procedures , and onlIn most of the reviewed articles, LC-MS was used to measure antimicrobial residues in wastewater ,52,53,54Japan has one of the highest sewage coverage rates in the world , and mosenterobacteriaceae, MDRA, MRSA, and VRE less efficiently than other bacteria. Staphylococci and E. coli show resistance to ozonation [Wastewater treatment processes in STPs vary widely in their ability to reduce ARB and ARGs . In Japazonation , and ARGzonation ,56. Furtzonation . Currentzonation ,76,77. Tzonation .To reduce the burden on STPs, pre-treatment of wastewater at its source before it enters the sewage system has attracted much attention . BecauseThe efficiency of STPs in removing antimicrobials from wastewater varies depending on the type of antimicrobials and the treatment method, e.g., the CAS process shows poor removal rates for trimethoprim, macrolides, and quinolones compared with penicillins and sulfamethoxazole ,48,53,54Our review suggests that most of the residual antimicrobials in sewage water in Japan are antimicrobials administered to hospital inpatients rather than those consumed by outpatients at home. Azuma et al.  comparedE. coli and ARGs, and can act as a reservoir that maintains levels of AMR in STP. Hospital effluent also contains clinically important ARB, as listed above, and can be an important source of them. The persistence in wastewater of antimicrobial residues above the PNEC values in wastewater may contribute to the selection and dissemination of AMR in the Japanese sewage treatment system. AMR in wastewater may affect humans and environments through direct exposure or indirect pathways through contaminated sludge. Because most of the studies used culture-based methods and almost none used culture-independent methods to assess the AMR in wastewater, ARGs in wastewater were poorly evaluated in Japan. The CAS process in combination with chlorination and ozonation, as commonly used in Japanese STPs, may be inadequate for completely removing AMR and antimicrobials in wastewater; therefore, to reduce the burden of AMR and antimicrobials on wastewater treatment systems, new strategies to treat AMR and antimicrobial agents in STP, including on-site source treatment and UV processes, need to be developed and introduced, and the proper use of oral antimicrobials needs to be ensured.Our review revealed that clinically important ARB, including ESBL-producing bacteria, CRE, MDRP, MDRA, MRSA, and VRE, and antimicrobial residues are present in human wastewater in Japan. The concentrations of the ARB and antimicrobials are quite low in Japan compared with those in LMICs because of the low prevalence of AMR, and they are further reduced by STP treatment before the wastewater is discharged into the river water. Nevertheless, the remaining ARB, along with ARGs (which cannot be degraded by STP treatment), could be an important source of contamination in river water. AS in STP contains various ARB, including drug-resistant This study has several limitations. First, because each article included in this review analyzed different types of ARB/ARGs and antimicrobials in different wastewater sources at different times with different methods, it is difficult to compare the data between articles. Second, no articles assessed the relationship between the presence of antimicrobials and the occurrence of AMR in wastewater. Third, none of the articles described the actual impact of AMR and antimicrobials in wastewater on the environment or the direct risk to human health in Japan, so which interventions effectively reduce AMR and antimicrobials in wastewater remains unclear. Nevertheless, our review showed the importance of the monitoring for AMR and antimicrobials in STP influent/effluent, STP sludge, river water, and hospital effluent and of the efforts to reduce the contamination load of AMR and antimicrobials in wastewater before it is discharged into the environment. Future actions and research to develop strategies to reduce the levels of AMR and antimicrobials in wastewater in HICs are warranted.


Background: The presence of heavy calcification in the coronary artery always presents a challenge for coronary computed tomography angiography (CCTA) in assessing the degree of coronary stenosis due to blooming artifacts associated with calcified plaques. Our study purpose was to use an advanced artificial intelligence  model to suppress the blooming artifact in CCTA and determine its effect on improving the diagnostic performance of CCTA in calcified plaques. Methods: A total of 184 calcified plaques from 50 patients who underwent both CCTA and invasive coronary angiography (ICA) were analysed with measurements of coronary lumen on the original CCTA, and three sets of ESRGAN-processed images including ESRGAN-high-resolution (ESRGAN-HR), ESRGAN-average and ESRGAN-median with ICA as the reference method for determining sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: ESRGAN-processed images improved the specificity and PPV at all three coronary arteries  compared to original CCTA with ESRGAN-median resulting in the highest values being 41.0%  and 26.9%  at LAD; 41.7%  and 36.4%  at LCx; 55%  and 47.1%  at RCA; while corresponding values for original CCTA were 21.8%  and 22.8% ; 12.5%  and 27.6% ; 17.5%  and 32.7%  at LAD, LCx and RCA, respectively. There was no significant effect on sensitivity and NPV between the original CCTA and ESRGAN-processed images at all three coronary arteries. The area under the receiver operating characteristic curve was the highest with ESRGAN-median images at the RCA level with values being 0.76 , 0.81 , 0.82  and 0.86  corresponding to original CCTA and ESRGAN-HR, average and median images, respectively. Conclusions: This feasibility study shows the potential value of ESRGAN-processed images in improving the diagnostic value of CCTA for patients with calcified plaques. Coronary artery calcium is considered a powerful tool for the prediction of coronary artery disease (CAD) risk when compared to traditional risk factors. Its use is integrated into the recent guidelines for further risk assessment, and its wider use in patient screening is suggested to allow for a more personalised risk assessment ,2. HowevRecently, artificial intelligence (AI)-based  image reconstruction and synthesis has become an active research area in radiology ,9. CommeHowever, other than denoising, the GAN approach is also useful for increasing image spatial resolution. In 2016, Ledig et al. reported a seminal work of super-resolution GAN (SRGAN) which was able to generate high-resolution photo-realistic natural images  based on low-resolution input images . SubsequAnonymised CCTA and ICA (reference) image datasets in Digital Imaging and Communications in Medicine (DICOM) format of 50 adult patients who undertook the CCTA in 2014 with calcified plaques identified in at least one coronary artery segment on the CCTA and subsequent ICA performed for coronary artery stenosis diagnosis were collected . Due to The CCTA images were acquired by a first-generation dual-source CT  , a second-generation dual-source CT  , and a 640-slice CT  . The followings were their reconstruction slice thicknesses and intervals: 0.6–0.75 mm and 0.5–0.6 mm , and 0.5 mm and 0.25 mm (640-slice CT), respectively. Details of the imaging protocols were reported previously. Our previous study showed that the use of CCTA images acquired by various systems did not have any significant difference in the diagnostic value, and hence, these CCTA images were considered suitable for this study [https://github.com/xinntao/ESRGAN, accessed on 1 February 2022. Unlike the traditional CNN approach  for super-resolution (SRCNN) [Generator: Removal of the batch normalization layers from the SRGAN with the addition of residual-in-residual dense block layers to facilitate training of a deeper model.Discriminator: Implementation of the relativistic average discriminator to determine whether an image was more realistic rather than “real” or “fake” noted in the SRGAN.Loss function: Determination of the perceptual loss based on the VGG features before its activation rather than after the activation employed in the SRGAN for better monitoring of image brightness and texture.The calcium deblooming in CCTA was performed through four times upscaling the size of the collected CCTA images from 512 × 512 pixels to 2048 × 2048 pixels with the use of the open-source ESRGAN model by Wang et al. . Its sou (SRCNN) ,12,13,20 (SRCNN) . Wang et (SRCNN)  further  (SRCNN)  SRGAN mohttps://data.vision.ee.ethz.ch/cvl/DIV2K/, accessed on 1 February 2022), Flickr2K with 2650 images , and OutdoorSceneTraining (OST) with 10,324 images . These datasets provided a great variety of colour photos with different resolutions (including 2k resolution) and scenes/objects such as mountains, buildings, animals and humans but unrelated to medical imaging. To increase the variety, these images were flipped and rotated randomly for training. Details of the ESRGAN are available from the article by Wang et al. [The ESRGAN model was trained by three publicly available image datasets, DIV2K with 800 images  for measurements of coronary stenosis caused by calcified plaques. The minimal lumen diameter (MLD) of three main coronary arteries, namely left anterior descending (LAD), left circumflex (LCx) and right coronary artery (RCA) was measured at each calcified plaque lesion using the same approach as described in our previous studies ,19. MeasICA as a reference method to determine the degree of coronary stenosis was performed in all these patients following the standard femoral or radial approach. The MLD was measured by the same observer in angiographic projections where calcified plaques caused the most severe narrowing of the above-mentioned three main coronary arteries. The interval between CCTA and ICA measurements was 4 weeks to avoid potential influence or biased opinion on measurement accuracy. Similarly, three measurements of the same calcified lesion were obtained, with the mean value averaged.To study the impact of ESRGAN on reducing the blooming artifact with resultant improvement in coronary lumen visualisation and assessment, we also compared the percentage of reduction of MLD in these measurements by subtracting the MLD from original CCTA images to ESRGAN-processed images, with differences divided by the MLD from the original CCTA images. This allowed us to determine the diagnostic value of ESRGAN-processed images for assessment of calcified plaques, in particular the reduction of false-positive rates caused by severe calcification in the coronary arteries as opposed to those from the original CCTA images.p-value of <0.05 indicating statistically significant.Statistical analyses were performed using SPSS 27.0 . Continuous variables were expressed as the mean ± standard deviation, while categorical variables were presented as percentages. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) for the detection or exclusion of significant stenosis (>50%) on CCTA were calculated and compared between original CCTA and ESRGAN-processed images with ICA as the reference. Receiver operating characteristic (ROC) analysis, including area under the ROC curve (AUC), was employed for comparison of diagnostic performance between these imaging methods. Three-way analysis of variance (ANOVA) with pairwise comparisons post-hoc tests was performed to compare differences among these measurements from 5 datasets  with A total of 184 calcified plaques from 150 coronary arteries of the 50 patients were identified for inclusion in the analysis. p < 0.001 for all comparisons: Original and ESRGAN-HR/ESRGAN-Average/ESRGAN-Median vs. ICA) . There were no significant differences in the MLD measurements among the ESRGAN-processed images , although ESRGAN-Median processed images resulted in better improvement or reduction of blooming artifact than the other two ESRGAN-processed datasets. p < 0.05). As shown in There were significant differences in the MLD measurements at these coronary plaques between original, ESRGAN-processed images, and ICA, with significant overestimation of the lumen stenosis due to the presence of calcified plaques on CCTA images (vs. ICA) . ESRGAN-The highest number of false positive rates was found in the original CCTA images resulting in the lowest specificity and PPV at LAD, LCx and RCA arteries as shown in This study tested the feasibility of using an advanced DL approach, ESRGAN to process the CCTA images with the aim of suppressing blooming artifact associated with heavy calcification in the coronary arteries. Our results show that ESRGAN-processed images improved coronary lumen visualisation with ESRGAN-Median approach resulting in up to 20% reduction of blooming artifact in the coronary lumen assessment. ESRGAN-Median processed images reduced the false positive rates by up to 33% when compared to the original CCTA images and this has significant clinical value with corresponding reduction of unnecessary invasive procedures, and potential of reclassification of patient risk assessment, and improvement in cost effectiveness regarding the judicious use of imaging modalities, in particular, reserving the downstream testing such as risky and costly ICA procedure only for patients with significant coronary stenosis.It is a well-known fact that CCTA has limited diagnostic value in assessing calcified plaques with low specificity and PPV ,4,6,7. AAI has become an increasingly used tool in the medical domain and its application in CAD including coronary stenosis and calcium scoring assessments has shown great promise. Studies have reported that AI, specifically, machine learning- (ML-) and DL-based CCTA image processing and analysis assists automatic segmentation and detection of coronary stenosis, with ML and DL-based risk assessment models improve risk stratifications and prediction of disease outcomes in patients with CAD ,33. ThesRecently, a study has reported the use of Canon AiCE reconstruction kernel to suppress the blooming artifact on the CCTA images. However, its findings show that the Canon AiCE reconstruction kernel could only achieve about 10% of PPV improvement for CCTA cases with 50% diameter stenosis or greater . In contBesides, our results show that the ESRGAN-processed images reduced the false positive rates, thus improving the specificity and PPV when compared to the original CCTA images. Among the three different ESRGAN-processed datasets, ESRGAN-Median shows the best outcomes. It is well known that the average and median (pixel) binning are common image processing techniques to suppress the image noise, thus improving coronary lumen visualisation for more accurate assessment ,22. HoweTechnological advancements in CT scanning techniques have enabled CCTA to quantitatively assess coronary plaques for risk classification and prediction of cardiac events. However, interpretation of CCTA images, in particular when characterizing high-risk coronary plaques depends on visual assessment and highly relies on a priori knowledge and experience of the reader (radiologist or cardiologist). It has been reported that the inter-observer reproducibility of assessing high-risk plaque features is poor even among experienced readers . This liOur feasibility study shows promising results of improved diagnostic performance of CCTA with the use of ESRGAN model, however, there were some limitations that existed in this study. First, the sample size was small as we only analysed 50 cases with calcified plaques. This limitation was compensated by the analysis of 184 plaques, which was greater than that of the study about the use of Canon AiCE reconstruction kernel for calcium deblooming , althougIn conclusion, we tested the ESRGAN model for suppressing the blooming artifact in CCTA images of 50 cases with calcified plaques in the coronary arteries and our preliminary results show that the ESRGAN-processed images improved the specificity and PPV compared to the original CCTA images. The ESRGAN-processed images suppressed the blooming artifact associated with severe calcification in the coronary arteries, thus reducing the false positive rates. This has significant clinical value in improving the CCTA performance when assessing calcified plaques. Future research with the use of the finetuned ESRGAN model is needed to further enhance the diagnostic value of CCTA for patients with the calcified plaques.


However, the CuCrO2 particles did not readily adhere to the surfaces of the CeO2 nanofibers, so a trace amount of SiO2 was added to the surfaces to make them hydrophilic. After the SiO2 modification, the CeO2 nanofibers were immersed in Cu-Cr-O precursor and annealed in a vacuum atmosphere to form CeO2-CuCrO2 nanofibers. The CuCrO2, CeO2, and CeO2-CuCrO2 nanofibers were examined by X-ray diffraction analysis, transmission electron microscopy, field emission scanning electron microscopy, scanning transmission electron microscope, thermogravimetric analysis, and Brunauer–Emmett–Teller studies (BET). The BET surface area of the CeO2-CuCrO2 nanofibers was 15.06 m2/g. The CeO2-CuCrO2 nanofibers exhibited hydrogen generation rates of up to 1335.16 mL min−1 g-cat−1 at 773 K. Furthermore, the CeO2-CuCrO2 nanofibers produced more hydrogen at lower temperatures. The hydrogen generation performance of these CeO2-CuCrO2 nanofibers could be of great importance in industry and have an economic impact.Hydrogen is the alternative renewable energy source for addressing the energy crisis, global warming, and climate change. Hydrogen is mostly obtained in the industrial process by steam reforming of natural gas. In the present work, CuCrO In this study, the CeO2 nanofibers and CeO2-CuCrO2 nanofibers were examined using the appropriate instrumentation techniques. By using an X-ray diffractometer  with a working voltage of 30 kV and CuK radiation, the crystalline structures of the nanofibers were examined. Field emission scanning electron microscopy (FESEM)  and transmission electron microscopy studies  were used in this work to examine the morphology and particle size of the catalyst. A thermogravimetric analysis/differential scanning calorimeter  was used to investigate the thermal degradation behavior of electrospun fibers. The specific surface area was calculated using the Brunauer–Emmett–Teller (BET) method using a Micromeritics TriStar II 3030 specification. Before a BET measurement was performed, an appropriate quantity of the produced catalyst was de-gassed at 473 K for 24 h to eliminate the absorbed water. At different relative pressures (P/P0) ranging from 0 to 0.3, N2 adsorption isotherms were observed and examined while the catalyst absorbed N2.The starting reagents, namely copper nitrate hexahydrate  and then annealed in air at 873 K. The modified CeO2 nanofibers were analyzed by XRD, SEM, and TEM studies.The surfaces of the CeO2 nanofibers decorated with CuCrO2 nanoparticles (CeO2-CuCrO2) were prepared with the following procedure. The CeO2 fibers were dipped in the precursor, which was a mixture of methanol, chromium nitrate, copper nitrate, and Triton X100, and then dried at 353 K for 2 min before being annealed at 1073 K in a vacuum with a heating rate of 283 K per minute. The prepared CeO2-CuCrO2 nanofibers were analyzed by XRD, SEM, and TEM.CeOThe steam reforming of methanol was performed in a tubular flow reactor using a 25 cm quartz tube with a 1.2 cm inner diameter, nitrogen as the carrier gas with a flow rate of 30 sccm, and 20 mg of catalyst per SRM reaction. The system was connected to a gas chromatograph for analysis. A methanol–water mixture was prepared in a 3:1 molar ratio and heated to 353 K on a hot plate to evaporate methanol–water vapor. A gas tube was inserted into the Erlenmeyer flask beneath the level of the methanol aqueous solution, and then the methanol vapor was carried by nitrogen to the catalyst for the reaction. The nanofibers were sandwiched between quartz cotton in the middle of the quartz tube and then heated to 523, 573, 623, 673, 723, and 773 K, respectively. A gas chromatograph (GC 1000 China Chromatography TCD) was used to analyze each temperature and identify the average values .2 nanofibers, showing the pure CeO2 cubic phase (JCPDS card PDF#34-0394.) After the electrospun CeO2 nanofibers were annealed at high temperature, the cerium nitrate decomposed and CeO2 remained.The prepared nanofiber diffraction patterns and crystal structures were studied by XRD studies and analyzed with powder X-ray diffractometric MDI JADE5.0 software tools. 2-coated CeO2 nanofibers. The XRD spectra of CeO2-CuCrO2 nanofibers exhibited that CuCrO2 nanoparticles were attached to the CeO2 nanofibers. The XRD pattern reveals CeO2 (PDF#34-0394) and CuCrO2 (PDF#39-0247) phases on the CeO2-CuCrO2 nanofibers. The XRD pattern of CeO2 nanofibers after SiO2 surface modification only reveals CeO2 (PDF#34-0394) due to annealing at 873 K and its SiO2 content being too low.2-CuCrO2 after SRM at different temperatures. From the XRD pattern, it can be observed that when the catalytic temperature increases, the peak of CuCrO2(101) at 2θ = 35.178°(PDF#39-0247) gradually disappears. At 773 K, due to the precipitation of CuCrO2 after catalysis, the peaks of the copper (111) and (200) planes can be observed at 2θ = 43.297° and 50.433° (PDF#04-0836), respectively.2 nanofibers and CeO2-CuCrO2 nanofibers were identified by FESEM and TEM studies. 2 and CeO2-CuCrO2 nanofibers. 2 nanofibers after annealing at a rate of 274 K/min to 873 K in an air atmosphere. The CeO2 nanofibers decreased in size by about 110 nm and disappear from PVP due to annealing. 2-CuCrO2 nanofibers. It was found that CuCrO2 is difficult to directly attach to the surface of CeO2; therefore, the surface was modified with SiO2 to improve the CuCrO2 adherence. The FESEM images show that the CeO2-CuCrO2 nanofibers were very thin, with diameters similar to those of the CeO2 nanofibers.The morphologies of the CeO2-CuCrO2 after SRM at different temperatures. The morphologies of CeO2-CuCrO2 nanofibers after SRM at 523–623 K exhibited the fiber structure, revealing that the CeO2-CuCrO2 nanofibers had better stability at lower reaction temperatures, as can be seen in 2-CuCrO2 nanofibers. These images confirmed that the CuCrO2 particles were arranged and attached to the surface of CeO2 nanofibers. The STEM image in 2 nanofibers had particles arranged on the surface and attached to them. The particle attached to the fiber were investigated by STEM-EDS mapping. The STEM-EDS mapping confirmed the presence of (b) Ce, (c) Si, (d) O, (e) Cu, and (f) Cr in the CeO2-CuCrO2 nanofibers. 2-CuCrO2 nanofibers. 2 nanofibers coated with CuCrO2. 2-CuCrO2 nanofibers. The STEM-EDS spectra of CeO2-CuCrO2 nanofibers after SiO2 surface modification show that (b) Ce, (c) Si, (d) O, (e) Cu, and (f) Cr were present in the CeO2-CuCrO2 nanofibers. 2-CuCrO2. Hence, TEM and STEM-EDS studies confirmed the formation of CeO2-CuCrO2 nanofibers.2-CuCrO2 fibers. According to the TGA/DSC studies, the weight loss before 373 K is due to the volatilization of the remaining water in the CeO2-CuCrO2 fibers. The slight weight loss at approximately 423 K and the exothermic slope are due to DMF decomposition, and the endothermic peak at around 493 K is due to cerium nitrate decomposition. After that, a massive and continuous weight loss at about 523 K indicates the significant decomposition of PVP. Moreover, at 573 K to 673 K, an endothermic peak signals the formation of CeO2-CuCrO2 nanofibers.To observe the decomposition mechanism of the as-spun nanofibers at high temperatures, a simultaneous thermogravimetric analyzer was used to observe the TGA/DSC curve, and the temperature was increased to 873 K at a rate of 283 K per minute in air. 2-CuCrO2 nanofibers is listed in 2-CuCrO2 nanofibers produced in this experiment is 15.06 m2/g, which is larger than the specific surface area of solid-state reactions and other electrospun products.The specific surface area of the CeO−1 g-cat−1. To obtain high catalytic performance, the prepared CeO2-CuCrO2 catalyst was heated without contact with methanol vapor. Additionally, the carrier gas was altered so that the system was filled with methanol steam, and the gas coming from the exit tube was located. As shown in 2-CuCrO2 catalysts at 523–673 K at a flow rate of 30 sccm. Additionally, when the reaction temperature was raised from 523–673 K, the rate of hydrogen generation increased. The results are shown in 2-CuCrO2 nanofiber exhibited an excellent hydrogen production performance compared with CuCrO2 (solid-state method) and commercial Cu/Zn/Al catalysts [2-CuCrO2 nanofibers at different temperatures—the hydrogen production rises with an increase in temperature. However, the CeO2-CuCrO2 nanofibers lose their activity at the reaction temperature is too high, therefore the experiment has not been continued to a high temperature. The CeO2-CuCrO2 nanofibers are extremely stable in air, in contrast to an H2-activated catalyst, which is typically harmful when exposed to air due to its high activity and potential for ignition and explosion. Therefore, there is no need to activate the CeO2-CuCrO2 nanofiber catalyst at high temperatures for the SRM process. This study implies that greater efficiency can be attained than with traditional catalysts if CeO2-CuCrO2 nanofibers are installed in a fuel cell vehicle. Future research will examine the stability of the catalyst, SRM conditions, and reactor condition optimization.A gas chromatograph was attached to the thermal conductivity detector and used for measuring the rate of hydrogen production. At a flow velocity of 30 sccm and temperatures between 523 K and 773 K, the hydrogen generation was measured using 0.04 g of catalyst, and the hydrogen production rate was converted into mL minatalysts ,35. Tabl2-CuCrO2 nanofiber catalyst was effectively created and used for steam reforming of methanol (SRM). The prepared nanofiber catalyst was evaluated by the field emission scanning electron microscope, transmission electron microscope, X-ray diffractometer energy-dispersive X-ray spectroscopy, thermogravimetric analyzer (STA), and Brunauer-Emmett-Teller analysis. The specific surface area of the CeO2-CuCrO2 nanofibers is 15.06 m2/g. The best hydrogen production rate of the CeO2-CuCrO2 nanofibers, 1335.16 mL min−1 g-cat−1, was achieved at a flow rate of 30 sccm and reaction temperature of 773 K. Furthermore, the optimization of reduction conditions and catalyst stability were studied. According to the findings, the increased hydrogen production rate can be ascribed to the stronger catalytic activity, larger surface area, lower reactor temperature, and higher methanol flow rate of the CeO2-CuCrO2 nanofiber catalyst. According to the H2 production performance, the CeO2-CuCrO2 nanofibers can be employed as a better catalyst for commercial H2 production and are suited for fuel cell vehicles without high-temperature activation.In this study, the electrospun CeO


Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument , which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years . The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient’s risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.The online version contains supplementary material available at 10.1007/s44192-022-00016-z. Suicides are preventable tragedies. The social, psychological and biological risk factors are increasingly being known –3. What Participants were enrolled in 2016-2017 in the study “Assessing Risk of Future Suicidality in Emergency Department Patients” The electronic medical record was reviewed by 3 psychiatrists independently. Scores for suicidality  were compared between the different scorers, with looking at all notes in the EMR that had been entered since each participant was initially enrolled in the first study in 2019 (with the first enrollment of patients being 6/17/2016). Following this the scores were normalized by time through finding the shortest length of time between enrollment date and chart access, and eliminating any scores performed at a longer duration than that shortest follow-up date.In addition to review of the EMR to gather information regarding suicidality, the Marion County Coroner’s Office database was also searched. None of the participants were found to have died by suicide locally.Following the collection of data a few separate analyses were performed. First, a Receiver Operating Curve Area Under the Curve (ROC AUC) for predicting future suicidality was calculated, for the 4-year follow-up. Second, a t-test was done comparing the CFI-S score between those with suicidality compared to those who did not have any over the 4 year follow-up. The analysis was also conducted at an individual item level. Third, a Pearson correlation analysis was done to determine how the CFI-S scores compare to the severity of suicidality. Suicidality was rated by severity in the following manner: those with suicidal ideation only received a score of 1, those with a plan received a score of 2, those with suicide attempt(s) received a score of 3, and those with hospitalization(s) for suicidality received a score of 4. Evaluating suicidality as a spectrum is supported by our previous work , 6, whicWe performed a machine learning-based suicidality classification using the CFI-S records as input. Each patient has a CFI-S record, e.g., answers to 22 yes/no questions. The input or feature of our machine learning models are vectors with length 22 + 3, i.e., 22 yes/no answers, total number of yes, total number of answers, and the CFI-S score. The experiment is designed as follows: for input data, we use the afore-mentioned features to feed in machine learning models, and the suicidality results  are converted to binary indicators to represent if a patient has suicidality or not. Therefore, to solve this suicidality classification problem we make use of five machine learning models, naive Bayes (NB) , XGBoostIn the binary classification of suicidality, we would like to predict if a patient will have or not any suicidality. In what follows, we further inspect those patients who have suicidality, and we predict two tasks: (i) how soon the patients would take such actions (imminence prediction), and (ii) how severe the behavior would be (severity prediction).In imminence prediction, we take the first time (in terms of month) of actions as imminence labels, and the input features are the same as in suicidality classification. For example, if a patient has suicidal ideation recorded 1 month after she/he take the CFI-S test, a suicide attempt recorded 3 months after, and a hospitalization recorded 4 months after, we take the earliest record and label the imminence as 1 month.In severity prediction, we take weighted severity as labels, i.e., severity is composed of four parts: ideation (SI), planning (SP), attempt (SA), and hospitalization (HP), and they weigh differently based on the severity of the action. More specifically, SI = 1, SP = 2, SA = 3, and HP = 4. Different from suicidality classification, imminence and severity prediction are regression problems, and we use DNNs for prediction and evaluate it by accuracy with prediction interval (PI), root mean squared error (RMSE), mean absolute error (MAE), R-squared, and standard deviation.x and ground truth label y, DNN takes x as input and predicts the output value y’. Then, the actuarial prediction made by DNN model is an interval: , where PI is prediction interval calculated as z*stdev .The accuracy with PI is used to calculate the accuracy for our regression problem. Assume we have a data point with feature The DNN we used in suicidality prediction is designed for classification with about 400 k parameters. The input layer has 25 neurons and there are two hidden layers with 64 neurons each. Each fully connected hidden layer is followed by a dropout layer with 50% keep rate and the ReLU  activatiThe DNN we used in imminence and severity prediction is designed for regression. The input layer has 25 neurons, and there are 2 hidden layers. The first hidden layer is a fully connected layer with 32 hidden neurons and Sigmoid  activatiyes and no are taken as 1 and 0, other answers are taken as -1. With these vectors, we calculate the cosine similarity between vectors as the weight between two nodes. With node representation and weights, we construct an all-to-all network first, and then delete connections between nodes according to a threshold in the edge’ weights, i.e., if the weights between two nodes are less than a threshold (0.995), we delete that link. In the end, only patients with very similar CFI-S records are connected with a link, and they are located closer to each other in the graph. Graph neural networks (GNNs) are advanced neural network architectures developed based on graph theory concepts [https://github.com/cmxxx/SI.We form a similarity network of all patients based on their CFI-S records. In this network, each node represents a patient, and the link between two nodes (patients) are the similarity between these two nodes. With the answers to the questionnaire, each patient is described by a vector of size 22 + 3, where concepts . We alsoAfter collecting four-year follow-up data through electronic medical records chart review, analyses were performed using simple statistical tools.p-value of 2.39 E−21  with a ROC AUC of 0.798 and a t-test p-value of 1.46 E−22  receiving a score of 1, suicide plan (SP) receiving a score of 2, suicide attempt (SA) receiving a score of 3, and hospitalization for suicidality receiving a score of 4. The Pearson’s correlation R-coefficient was 0.44, p-value of 7.53 E−03 and a one tailed t-test with a value of 3.76 E−03  was perceived uselessness . The next top items, in order, were past suicidality (1.57 E−14), social isolation (2.40 E−14). hopelessness (6.17 E−13), and past history of a mental health diagnoses (9.54 E−13).A out Fig. d. The toMachine learning has the ability to extract more out of data, and it has been used for various medical diagnosis, such as tree-based models in PTSD assessment , naïve BThe future suicidality prediction is formulated as a binary classification problem. We developed a deep neural networks (DNN) framework, and compared it with other classical machine learning classifiers—native Bayes (NB), XGBoost (XGB), random forest (RF), support vector machines (SVM).The receiver operating characteristic (ROC) curve, accuracy, precision, recall evaluation metrics, F1 score, and area under receiver operating characteristic (AUROC) results in Fig. In addition to classical machine learning and deep neural network classifiers, we constructed patient similarity networks  elapsed between the CFI-S assessment and the first instance of suicidality of a patient. We used our DNN framework to investigate these two regression problems. Figure A simple, easy to administer, 22 items polyphenic risk score scale for suicidality, the CFI-S, which encompasses known risk factors but does not ask about suicidal ideation, was administered in an Emergency Department setting. The score was predictive of suicidality over the long-term, i.e., the ensuing 4 years of follow-up, with an AUC of 80% and a Hazard Ratio of 1.33. This simple tool, which on average took 5 min to administer, can be used in any setting, and provide a personalized mitigation plan by looking at the items that tested positive. Of note, the CFI- S does not ask about suicidal ideation, unlike the Beck Scale  or the CMachine learning approaches boosted the predictive ability of CFI-S up to 90%. By combining these analyses with a network science visualization, we developed a similarity network classifier for visualizing patient’s risk. Our CFI-S-based graph has two main components: a smaller area located in the lower left corner, which represents patients of “high-risk” suicidality, and a larger area located in the upper right corner of the graph, which represents patients with a “low-risk” of suicidality. This can be used for early-stage screening of suicidality risk, by showing where a new individual fits, based on the CFI-S risk score, compared with a well-studied normative cohort such as ours. For instance, one can exploit our framework for developing an AI system that can analyze the CFI-S answers of a patient during a short (5 min) interview, estimate the higher-order similarity scores between these newly recorded CFI-S scores and those in the cohort of patients prone to suicidality, visualize the position of the patient on the graph and determine a personalized strategy to mitigate suicide risk. Our proposed framework can be extended to encompass multiple data modalities , in order to identify additional signatures of cognitive, physiological and behavioral nature that can help better predict suicidality.Besides CFI-S, other types of information about the patients  could be infused in the future into the machine learning framework to improve the model performance. For instance, the time spent on each CFI-S question, and facial expression images or videos, could be useful in inferring the emotion and trustworthiness of CFI-S answers. Genomic biomarker data, and other mental health phenotypic data, could also be integrated alongside the CFI-S, in a bio-psycho-social algorithm. This is the direction our future collaborative work is taking.Lastly, it is particularly interesting, and actionable for preventive approaches, that feeling useless and socially isolated are top risk factors identified by our studies. The version of the instrument used at that time for this study had 22 items. We are developing now an expanded version that has 33 items, with more suicidality risk factors included. This version has not been yet been fully analyzed and compared to the original version, but may reveal in future studies additional actionable risk factors.Supplementary file1 (DOCX 130 KB)Below is the link to the electronic supplementary material.


Colorectal cancer is a heterogeneous disease. Several efforts have been made to characterize this heterogeneity but they have no impact in the clinic. In this work, we used a novel analysis approach based on identifying layers of information using expression data from colorectal tumors and characterized three different layers of information: one layer related to adhesion with prognostic value, one related to immune characteristics, and one related to molecular features. The molecular layer divided colorectal tumors into stem cell, Wnt, metabolic, and extracellular groups. These molecular groups suggested some possible therapeutic targets for each group. Additionally, immune characteristics divided tumors into tumors with high expression of immune and viral mimicry response genes and those with low expression, suggesting immunotherapy and viral mimicry-related therapies as suitable for these immune-high patients.Colorectal cancer (CRC) is a molecular and clinically heterogeneous disease. In 2015, the Colorectal Cancer Subtyping Consortium classified CRC into four consensus molecular subtypes (CMS), but these CMS have had little impact on clinical practice. The purpose of this study is to deepen the molecular characterization of CRC. A novel approach, based on probabilistic graphical models (PGM) and sparse k-means–consensus cluster layer analyses, was applied in order to functionally characterize CRC tumors. First, PGM was used to functionally characterize CRC, and then sparse k-means–consensus cluster was used to explore layers of biological information and establish classifications. To this aim, gene expression and clinical data of 805 CRC samples from three databases were analyzed. Three different layers based on biological features were identified: adhesion, immune, and molecular. The adhesion layer divided patients into high and low adhesion groups, with prognostic value. The immune layer divided patients into immune-high and immune-low groups, according to the expression of immune-related genes. The molecular layer established four molecular groups related to stem cells, metabolism, the Wnt signaling pathway, and extracellular functions. Immune-high patients, with higher expression of immune-related genes and genes involved in the viral mimicry response, may benefit from immunotherapy and viral mimicry-related therapies. Additionally, several possible therapeutic targets have been identified in each molecular group. Therefore, this improved CRC classification could be useful in searching for new therapeutic targets and specific therapeutic strategies in CRC disease. Colorectal cancer (CRC) has been identified as the most prevalent tumor type. According to GLOBOCAN, there were 19.3 million new cases and 10 million cancer deaths worldwide in 2020. CRC ranks third in terms of incidence, representing 10% of new cancer cases, but the second in terms of mortality, with 940,000 estimated deaths .Until 2015, different genetic classifications for CRC had been proposed. In that year, the international CRC Subtyping Consortium (CRCSC) reached a consensus on the molecular genetic expression subtyping of CRC using a pooled molecular genetic analysis of 4151 colon tumors. Four colon cancer consensus molecular subtypes (CMS) were identified: CMS1 , CMS2 , CMS3 , and CMS4 . However, 13% of the samples could not be classified into any of the four described molecular subtypes . These uComputational analyses applied to high-dimensional omics data allow a deeper characterization of the molecular and immune features of tumors. Probabilistic graphical models (PGMs) have been previously used to identify differences in biological processes among several tumor types ,16,17,18The main objective of this study is to expand the knowledge about the molecular classification of CRC, according to the different biological realities of the tumor, with the aim to increase the clinical value of the already established molecular groups.limma R package [Three colorectal tumor gene expression databases  were analyzed. The resulting database was processed, removing control and duplicated probes. For this purpose, the variance of each probe was calculated and the most variable probe per gene was chosen. In addition, the batch effect due to the combination of independent databases was corrected using the  package . Informa package . FinallygrapHD package [Homo sapiens” was used as a background and categories Biocarta, GO-FAT, and KEGG were selected.First, those genes with a higher standard deviation in their expression across the dataset (standard deviation > 2) were selected to build the PGM, as previously described . The ana package  and R v3 package . PGMs ar package . “Homo sTo make comparisons between groups of samples, functional node activities were calculated as previously described . Brieflysparcl package [Consensus Cluster Plus package [Sparse K-means and the consensus cluster algorithm (CC) were used to explore the molecular information of CRC samples, as previously described . Sparse  package  and CC w package  and R v3 package .Then, we used the biological layer information to establish different classifications based on different tumor features. Differential expression patterns among groups were analyzed by Significance Analysis of Microarrays (SAM), defining a false discovery rate (FDR) below 5% . These ap-values were two-sided and considered statistically significant below 0.05.GraphPad Prism v6 was used for basic statistical analyses. Network visualization was done in Cytoscape software . DiffereGene expression datasets  from the Gene Expression Omnibus repository  were merRNA-seq data from eight hundred and five CRC patients were used in this study. Clinical characteristics of the cohort are summarized in In total, 132 samples were assigned to CMS1 (16.3%), 315 samples were assigned to CMS2 (39%), 97 samples were assigned to CMS3 (12%), and 188 samples were assigned to CMS4 (23.3%). Thus, 73 samples (9.4%) were not assigned to any CMS.A PGM was built with the gene expression profiles of the 1700 more variable genes. Seeking functional structures, 13 functional nodes with an overrepresented biological function were defined: immune, adhesion, inflammatory response, somatic stem cell, extracellular matrix, cellular response, extracellular response, nucleus, Wnt signaling pathway, plasmatic membrane, regulation of cardiac conduction, transport, and metabolism .The sparse K-means–CC workflow analyses identified nine biological layers. Each layer was split into two groups, with the exception of the second one, whose optimal classification was found to be in three groups. Each layer’s main function was characterized through gene ontology . Layers p = 0.0098, HR = 0.42, 95%CI = (0.21–0.81)) ) . Additio1–0.81)) B. No difIn order to perform a deeper characterization, differences between high- and low-adhesion groups were evaluated by functional node activity, as defined by the PGM . The higp = 0.57, HR = 0.82, 95%CI = (0.42–1.59))  . The CC 2–1.59)) . No diffFunctional node activity analysis showed that tumors in the immune-high group had higher expression of genes in the immune, inflammatory, cellular adhesion, and extracellular matrix functional nodes. On the contrary, immune-low tumors had higher expression of genes located in the transport, Wnt signaling pathway, nucleus, and regulation of cardiac conduction functional nodes .As the viral mimicry response has gained relevance in the last few years in cancer related to immune response activation , we studThe molecular layer resulted from merging the second, fourth, and sixth layers and it was divided into four groups by CC. Differences between molecular groups were evaluated using the PGM and the node activities. These analyses showed that molecular group 1 had higher activity in stem cell, nucleus, regulation of cardiac conduction, and transport nodes; molecular group 2 had higher activity of metabolism nodes; molecular group 3 had higher activity of nucleus and Wnt signaling pathway nodes; and molecular group 4 had higher activity of cell adhesion, extracellular matrix, and extracellular response nodes. Therefore, molecular group 1 , which presented the highest activity for the stem cell functional node, was designated as the stem cell group. Molecular group 2 , which had the highest activity of metabolism nodes, was named the metabolic group. Molecular group 3 , which had the highest activity of Wnt signaling pathway nodes, was named the Wnt pathway group. Finally, molecular group 4 , which had the highest activity of extracellular response and extracellular matrix nodes, was named the extracellular group .p < 0.0001), with the stem cell and Wnt groups mainly composed of distal tumors and the metabolic and extracellular groups by proximal tumors. No differences regarding the distribution of tumor stages across molecular groups were found.The stem cell node contained genes involved in stem cell maintenance, such as VANGL2 or PBX1. The metabolic node was formed by genes directly involved in metabolism, such as PHGD or PSAT1, and other genes, such as CTSE or REG4. The Wnt pathway node contained genes involved in the Wnt signaling pathway: RNF43, DKK4, LRP4, AXIN2, etc. The extracellular matrix node was mainly formed by collagens. Regarding the association of the defined molecular groups with clinical parameters, the tumor location was distributed significantly different between molecular subtypes ) ) .Once we obtained three independent classifications, we compared them with the CMS. Patients belonging to CMS1 and CMS4 were mostly immune-high, whereas CMS2 and CMS3 patients were mostly immune-low. The adhesion layer divided the CMS1 patients by half and most of the CMS4 patients were included in the high-adhesion group, whereas CMS2 and CMS3 were included in the low-adhesion group. According to the molecular layer, most of the CMS1 patients belonged to the extracellular molecular subtype, the CMS2 to the Wnt pathway, and the CMS3 to the metabolic group .Colorectal cancer is a molecularly and clinically heterogeneous disease with high rates of incidence and mortality . Both CRPGMs have demonstrated their utility in the analysis of tumor omics data, being able to structure molecular information from a functional point of view ,14,40. AThe adhesion layer has been divided into two groups, high and low adhesion, and has prognostic value, with the group of patients with low adhesion having the best prognosis. Distant CRC metastatic tumor formation is considered to be strongly influenced by the stable adhesion of cancer cells to the small blood vessel walls . In receImmunotherapy relies on harnessing the body’s immune system to kill cancer cells  and it hThe immune-high group had also overexpression of genes involved in the viral mimicry response. The viral mimicry response is a cellular state of active viral response triggered by endogenous stimuli instead of viral infection, in the case of cancer, triggered by retrotransposons . Viral mThe molecular layer has been divided into four groups: stem cells, Wnt pathway, metabolic, and extracellular. Cancer stem cells (CSCs) can regulate cancer invasion, distant metastases, and therapy resistance in CRC, as well as contribute to cancer recurrence in patients . The steRNF43 have clinical relevance because they implicate novel therapeutic options in CRC. Preclinical studies have shown that mutations in RNF43 make Wnt-induced cancer cells susceptible to the pharmacological inhibition of Wnt signaling by porcupine. Porcupine is an O-acetyltransferase that is part of the Wnt pathway, and could be postulated as a possible therapy in this type of Wnt-induced tumor [The Wnt signaling pathway plays an important role in the pathogenesis of CRC . Tumors ed tumor ,60,61. Ted tumor ,63. Porced tumor In tumors of the metabolic molecular group, the high expression of genes that play an essential role in cancer-specific metabolic reprogramming, such as PHGDH, has been observed . PHGDH iThe extracellular molecular group was characterized by the high expression of collagens, which could be one of the reasons that it is the subtype with the worst prognosis. Among the extracellular matrix adhesive components, type I collagen is one of the most important factors regulating cancer-related events at different tumorigenesis stages  The COL1We compared our three classifications of CRC tumors with the classification through CMS groups. The CMS classification mixed immunological, histological, and molecular information. The present study has been able to corroborate some of the molecular characteristics defined in the CMS, but it has also been possible to identify two layers of information that are independent of the molecular features of the CRC tumors related to adhesion and the immune status. These different levels of information complemented the molecular characteristics exposed in the CMS, and it has also been possible to add new information that allows patients with different CMS to benefit from the same therapeutic strategy.For instance, although the CRCSC determined that all CMS1 patients were immune-positive , our resAs for the CMS2 group, CRCSC showed that this group presents close similarity to the classical model of CRC carcinogenesis with activation of the WNT and MYC signaling pathways . AlthougAs for the CMS3 group, the CRCSC established that only patients in this group could benefit from possible therapies with PHGHD and PSAT1 or other metabolism-related molecules. Overall, 24.7% of CMS3 patients do not correspond to the metabolic molecular subtype, so other therapeutic options should be explored for these patients, since, as seen in this study, they are not characterized by the high expression of genes related to metabolism, even though they have been included in CMS3.Regarding the CMS4 group, our analysis determined that 74% of CMS4 patients were immune-high. This fact is consistent with the CRCSC classification, which described the relationship between CMS4 patients with the presence of high infiltration of cytotoxic T cells  and highOn the other hand, CMS4 is the subtype with the worst prognosis . RegardiTo summarize, this study allows us to minimize the percentage of patients without a specific treatment, since the layer classification allows the inclusion of information about the immune and adhesion status. Thus, patients of the stem cell molecular subtype and the Wnt molecular subtype could benefit from porcupine inhibition therapy, patients of the metabolic molecular subtype from possible therapies related to REG4 and CTSE, and patients of the extracellular molecular subtype from possible therapies related to COL1A1. On the other hand, the classification of patients by immune subtype, independently of CMS, provides valuable information to select the most suitable patients for immunotherapy treatment and viral mimicry therapies.The study has some limitations. First, validation of all the obtained classifications in an independent CRC cohort is needed. In addition, validation of the proposed therapeutic strategies for each group in cell cultures, organoids, or murine models should be performed. Moreover, only 12% of the tumors were stage IV, so these were underrepresented, as happened in the CMS study. However, this is the group in which possible molecular targets are most interesting because of their potential therapeutic utility. The study used a retrospective cohort, performed prior to immunotherapy administration, which may have changed the prognosis of some patients, especially those with microsatellite instability. Finally, these groups should be studied in the context of other clinical biomarkers, such as RAS/RAF or microsatellite instability.In conclusion, the generation of a classification of colorectal cancer according to the different biological realities of the tumor using probabilistic graphic models and layer analysis allowed the identification of four molecular subtypes of colorectal cancer and established two extra independent classifications based on adhesion and immune features, respectively. These classifications may help researchers and clinicians to search for new therapeutic targets and more specific treatments.


This Research Topic gathers the recent data on etiological factors, pathogen-host interactions, pathogenesis, diagnosis, epidemiology and control of infectious diseases of the swine respiratory tract. Pig production is one of the fastest-growing livestock sectors in the world. Intensification of pig production and globalization are important risk factors in increasing previously uncommon diseases, changes in their spread and the emergence of new infections. Despite the enormous progress in veterinary medicine, bacterial and viral respiratory diseases in pigs still constitute a group of diseases of particular importance in the profitability of production.Actinobacillus pleuropneumoniae (APP), porcine reproductive and respiratory syndrome virus (PRRSV) pathology, diagnosis, epidemiology, and control and eradication and some new data regarding Haemophilus parasuis (currently known as Glaesserella parasuis).This Research Topic brings together 8 articles with updated knowledge, including Stringer, Bossé et al.) presents a quick and straightforward recombinase-polymerase-amplification (RPA) isothermal technique for the detection of APP in clinical samples. Current gold-standard diagnostic methods lack sensitivity , require expensive thermocycling machinery (PCR) and are time-consuming (culture and PCR). The search for new, simple and reliable diagnostic methods is therefore welcome. The test described in this Topic achieved an excellent sensitivity (10 copies/μL), and clinical sensitivity and specificity reached 84.3 and 100%, respectively. These results indicate that the APP-RPA assay presents a suitable rapid and sensitive screening tool for this important veterinary pathogen. In the second paper on the Topic, Stringer, Li et al. described the validation of Whatman FTA® cards, a specialist form of filter paper containing a chemical coating that lyses cells, denatures proteins, and binds nucleic acids, preventing their degradation by oxidation or UV radiation, for collection and processing of APP isolates, or porcine lung tissue samples, for direct use in diagnostic multiplex PCRs. This study provides simple, efficient, rapid, and practical sample processing for APP detection and molecular serotyping.APP is one major bacterial porcine respiratory tract pathogen causing disease outbreaks worldwide, although effective commercial vaccines are available. The first article on the Topic  in pigs with PRDC symptoms are presented. Porcine respiratory disease complex results from multiple viral and/or bacterial co-infections. Porcine circovirus type 2 (PCV2), PRRSV, Mycoplasma hyopneumoniae and Influenza A viruses (IAV) are the main causative agents. PPV2 has been identified in healthy and clinically diseased pigs at a high prevalence worldwide. However, the significance of PPV2 infection in PRDC and its association with other co-infections are unclear. In the study described within the Topic, PPV2 was detected in the lung tissue in 39% of PRDC-affected pigs by qPCR. PPV2 infection was localized in alveolar macrophages and other cells in the lungs, with interstitial pneumonia in 28.2% of samples. Together, these results suggest that PPV2 is associated but may not be the sole causative agent with PRDC, warranting the control and prevention of this underdiagnosed virus.In the article by van Dixhoorn et al. provide interesting insights into the relationships between external factors, animal-based factors, and clinical outcomes of disease. This work provides a first basis to unravel the complex network of interactions that will enable a quantitative prediction of disease outcomes in pigs and may inspire further research in this area.Developing strategies to accurately predict the expected degree of disease severity before the onset of the disease is a significant challenge for farmers and veterinarians. In conclusion, the articles in this Research Topic discuss important features of porcine respiratory diseases and how to diagnose, prevent and control them. We hope that readers will find these articles interesting and inspiring.MP-M and AD wrote the draft and final version of the editorial. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.