From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.Characterization of protein complexes,  A protein complex is a group of proteins that interact with each other to perform a particular function in a cell, the basic biological unit of all living organisms. Some examples include the elaborate multiprotein complexes of mRNA transcription and elongation helping with gene regulation and key cytoskeletal protein complexes, such as microtubules with their trafficking proteins which help establish major structural elements of cells. Extensive biological experiments have investigated the physical interactions between proteins, and these have been modeled via weighted protein-protein interaction (PPI) networks, where a protein-protein edge weight corresponds to the strength of evidence for the protein-protein interaction. Disruption of protein-protein interactions often leads to disease, therefore identifying a complete list of protein complexes allows us to better understand the association of protein and disease. All experimental protocols for detecting complexes  have a tendency to miss interactions  and may also predict extra interactions . Proteins may also participate in more than one complex, potentially blurring the boundaries of otherwise unrelated protein communities. Computational analysis of protein-protein interaction networks can therefore be very useful in identifying accurate protein complexes and will help augment and direct experimental methods.V, E), where the set of nodes or vertices V represents the proteins, and the set of weighted edges E represents the strengths of evidence for interactions between proteins. Any group of nodes and edges that can be characterized as a protein complex can be referred to as a community; community detection methods can be used in turn to identify protein complexes.The weighted PPI network or graph G can be represented as pairs of nodes and edges  and inter-cluster density δext (C) are given bync and mc are the numbers of nodes and edges in subgraph C, respectively, and n is the number of nodes in graph G.A standard guideline for defining communities  is that However, there exist many communities that do not follow this criterion but can be identified by different properties they exhibit. One such example is a star-like topology, where one central node interacts with several nodes in yeast protein-interaction networks , as, forExisting community detection methods have primarily tried to optimize for high scores of community fitness functions, such as that of i.e., community representations in vector space, obtained by extracting topological and domain-specific features from communities. The community fitness function learned can then be used to select candidate communities from the network and evaluate them. Since finding maximally scoring communities in a network is an NP-hard  problem [When there are sufficient data available on known communities, rather than applying a generic community fitness function to the problem, it can be more accurate to learn a community fitness function directly from known communities. Then, new communities detected with the learned community fitness function can be expected to better resemble known communities in the field. Supervised machine learning methods are well suited for this purpose, and a few methods have been used to learn a community fitness function from constructed community embeddings,  problem , heuristExisting supervised methods use different machine learning methods to learn the community fitness function after extracting different features and use different heuristic algorithms to select candidate communities. The first supervised method  used a sRegarding scalability, the above methods have generally only been implemented on small yeast protein complex datasets, except for ClusterEPs, which trains on yeast data and tests on human PPIs.  implemenhttps://sites.google.com/view/supercomplex/super-complex-v3-0.In this work, we present Super.Complex , an end-to-end highly scalable (to large networks that fit on a disk), distributed, and efficient community detection pipeline that explores multiple supervised learning methods with AutoML (Automated Machine Learning) to learn the most accurate community fitness function from known communities. Super.Complex then samples candidate subgraphs in parallel by seeding nodes or starting with maximal cliques and growing them with an epsilon-greedy heuristic, followed by an additional heuristic such as iterative simulated annealing or pseudo-metropolis using the learned community fitness function. On a yeast PPI network, Super.Complex outperforms all 6 existing supervised methods, as well as 4 unsupervised methods. Three novel evaluation measures are proposed to overcome certain shortcomings of existing metrics. We apply Super.Complex to hu.MAP, a human protein-protein interaction network with ~8k nodes and ~60k edges to yield 1028 protein complexes, including high-scoring previously unknown protein complexes, potentially contributing to new biology, and make all data, code, and interactive visualizations openly and freely available at e.g. ϵ ϵ ϵ-greedy + iterative simulated annealing. This is followed by a (iv) post-processing step of merging highly overlapping communities. Finally, in the last step, evaluation, the learned communities are compared with known communities. The steps of the pipeline are fairly independent and can be improved on their own with methods to test the accuracy/performance of each of the steps.The pipeline Super.Complex comprises two main tasks, first, learning a community fitness function with AutoML methods, and second, using the community fitness function to intelligently sample overlapping communities from a network in parallel. As shown in First, the weighted network under consideration is cleaned by removing self-loops, as we do not consider interactions with oneself as a feature of communities. For scalability, the graph is stored on disk as a set of files, each corresponding to a node and containing a list of the node’s neighbors via weighted edges.Super.Complex takes sets of nodes comprising known communities and obtains their edge information from the induced subgraph of these nodes on the weighted network. Nodes in communities that are absent from the network are removed. Communities with fewer than 3 nodes, communities that are internally disconnected, and duplicate communities are also removed. Constructing the final set of positive communities involves 2 main steps: (i) merging similar communities, and (ii) splitting them into non-overlapping train and test sets. Note that if independent train and test sets of communities are known in advance, these steps can be skipped.j. We recommend users to set this value based on domain knowledge of observed redundancy in the set of known communities.In the first step, using a merging algorithm we devised, we merge highly similar communities to yield a final list of communities, where no pair of communities have a Jaccard score  greater i.e., with only a small number of merges performed on the original set of known communities. This is especially important in applications with limited data, such as the human and yeast protein complex experiments in this work. Our algorithm was designed with this objective in mind, and works as follows. The iterative algorithm makes multiple passes through the list of communities performing the merging operation until the specified criterion is achieved. In a single pass of the list of communities, each community is considered in order and merged with the community with which it has the highest overlap  and the list is updated immediately by removing the original 2 communities and adding the merged community to the end of the list, so that the updated list is available for the next community in consideration. This merging algorithm achieves a lesser number of merges than a trivial merging solution which would merge random pairs of communities that do not satisfy the required criteria until convergence. In practice, the proposed algorithm quickly converges to a solution .Multiple solutions exist that achieve this goal, however, we want a solution with a large number of communities, In the second step, the communities are split into non-overlapping training and testing datasets, to emphasize their independence. We obtain sets with equal size distributions and a 70–30 train-test split, as recommended for machine learning algorithms with a small amount of data. Previous algorithms such as  and SupeNegative communities, or non-communities are represented by random walks sampled from the network by growing random seeds, adding a random neighbor at each step. The number of steps ranges from the minimum size to the maximum size of positive communities, with a total number of random walks equal to the number of positive communities multiplied by a scale factor > 1. The random walks are split almost equally across all the sizes, by splitting equally across the different number of steps to be taken for a random walk, to yield an almost uniform size distribution for negative communities. We say almost uniform size distribution, as random walks with the same number of steps need not yield the same sizes, given that the random walk as defined here can revisit edges it has already visited. To achieve random walks of the same size, the algorithm attempts an extra number of random walks and an extra number of steps to achieve the desired random walk size.The size distribution of positive communities is taken into consideration while training the machine learning model when using a uniform distribution for negatives. We also explore using almost the same size distribution as the positive communities to construct the negative communities. For this, for each size of the positive communities, we construct the negatives by sampling a number of random walks equal to the scale factor times the number of positive communities of this size. However, in this case, we find that there are quite a few missing sizes due to limited positives which may affect the scoring of subgraphs of the missing sizes. Using a uniform distribution would provide more information to learn a more accurate community fitness function that can recognize negatives at sizes missing for positives. In the following feature extraction step, random walks resembling communities are removed. The final number of negative communities is close to the number of positive communities, as we have sampled a slightly higher number of random walks via the scale factor.i.e. the positive and negative community embeddings.As communities exhibit different topological structures on the graph, these can be learned by considering useful topological features of communities. Based on graph theory, we extract 18 topological features, detailed in A community fitness function is learned as the decision function of a binary machine learning classifier trained to distinguish the community and non-community embeddings constructed in the previous feature extraction step. For this, we use an AutoML algorithm, TPOT , a genetWe specify 6 pre-processors that scale the feature matrix. These are - (i) Binarizer, which sets a feature to 0 or 1 based on a threshold, (ii) MaxAbsScaler, which divides the feature by the maximum absolute value of the feature, (iii) MinMaxScaler, which subtracts the minimum of the feature from the feature vector and divides by the range of the feature, (iv) Normalizer, which divides the feature vector by its norm to get a unit norm, (v) RobustScaler, which makes a feature robust to outliers by scaling using the interquartile range and (vi) StandardScaler, which standardizes to the Z-score by subtracting the mean and dividing by the standard deviation of the feature.We include four feature selecting pre-processors, which are additionally important as we incorporate 6 additional preprocessors that construct combined features. The additional preprocessors include - (i) Decomposition: PCA , FastICA , (ii) Feature Agglomeration, (iii) Kernel Approximation methods: Nystroem, Radial Basis Function RBFSampler, (iv) Adding Polynomial Features, (v) Zero counts: Adds the count of zeros and non-zeros per sample as features and (vi) OneHotEncoder for numeric categorical variables. The feature selecting preprocessors include - (i) SelectPercentile, which selects the highest-scoring percentage of features based on 3 univariate statistical tests, FPR - False Positive Rate, FDR - False Discovery Rate and FWE - Family-wise error rate; (ii) VarianceThreshold which removes low variance features, (iii) RFE (recursive feature elimination) using ExtraTrees and (iv) SelectFromModel using ExtraTrees based on importance weights. The ML models included are - (i) Naive Bayes methods using Gaussian, Bernoulli, and Multinomial distributions (ii) Decision Trees, (iii) Ensemble methods of ExtraTrees, Random Forest, Gradient Boosting and XGB (XGBoost), (iv) K-nearest neighbors, (v) Linear SVMs and (vi) Linear models for Logistic Regression.The population size and number of generations are provided as parameters for the genetic algorithm of the AutoML pipeline. In practice for our application, giving a value of 50 for each yielded good results. There is an option for a warm start, where you can run additional generations and with additional population sizes starting from the latest results, if the results are unsatisfactory. Additionally, several other machine learning models and preprocessors can also be incorporated into this pipeline, including neural networks. Note that in our experiments, we also obtained pipelines that stack different ML models. We run the pipeline in a distributed manner, setting the number of jobs as the number of processes that run in parallel on a single computer. All the processes on the computer can be used for maximum utilization, however, the documentation notes that memory issues may arise for large datasets. In practice, we set the number of jobs as 20 on a Skylake compute node (Intel Xeon Platinum 8160 with 48 cores @2GHz clock rate).By default, 5-fold cross-validation is performed, although this can be modified by a parameter. The pipelines with high cross-validation average precision scores (area under the PR curve) are evaluated on the test dataset to find the best pipeline for our data, to use this for the community fitness function. A one hidden-layer perceptron is also available for training, and comparison with the AutoML output to select the best model. We evaluate the performance of the ML binary classifier using accuracies, precision-recall-f1 score measures, average precision score, and PR curves for the test sets while also evaluating these measures for the training set to compare with the test measures and check the bias and variance of the algorithm to make sure it is not underfitting or overfitting the data. We also plot the size-wise accuracies of the model to understand how a model performs w.r.t to the size of the subgraph it is evaluating.Finding a set of maximally scoring candidate communities in a network is an NP-hard problem, as proved by  by reducjoblib python library. Each process intelligently grows a single seed node into a candidate community and writes it to the compute node’s temporary storage. For this, we need the graph and parameters of the community fitness function, which we store on temporary disk space of each compute node to optimize RAM as it is impractical to store large networks and machine learning models in memory. Each process reads the model into its memory and uses it to evaluate the neighbors, to pick the neighbor to add to the current subgraph in the growth process from the seed node. The neighbors of the subgraph under consideration at each step of the growth are read from disk on-demand and stored in memory only until they have been evaluated by the fitness function. In this way, we ensure that the processes have a low memory footprint, which can otherwise quickly become a bottleneck for large graphs. We also minimize disk storage by storing each resulting candidate community compactly using only its nodes, as its edges can be inferred if/when necessary by inducing the nodes on the graph. After all the child processes of growing seeds complete on a compute node, the compute node reads the set of learned community files it had stored on its disk and compiles them into a list of candidate communities before writing the list to the file server. The same code also runs in a distributed setting with only one multi-core compute node. There also exists a serial option to run the code without invoking parallel constructs, useful for running on a single core.First, we need to select seeds. Options for seeds include specifying all the nodes of the graph (recommended for best accuracy), all the nodes of the graph present in known communities, a specified number of nodes that will be selected randomly from the graph, or maximal cliques. In the distributed setting using multiple compute nodes, the specified seeds are partitioned equally across compute nodes, and each compute node deals only with the task of growing the seeds assigned to it. In practice, the partitioning is done by a main compute node which partitions the list of seeds and stores the partitioned lists as separate files on the file server. Then it launches one task per compute node (including itself) using the launcher module , where at1, if the number of neighbors of the current subgraph is greater than the threshold, a random sample of the neighbors equal to the provided threshold is chosen for evaluation. Now, of the neighbors, first, an ϵ-greedy heuristic is used to select the neighbor to add to the subgraph. In an ϵ- greedy heuristic, with ϵ probability, a random neighbor is added instead of the maximum scoring neighbor.Only for the first step of growth, we add the neighbor connected with the highest edge-weight. We provide 2 options for growing the subgraph at each step- an exhaustive neighbor search that is suitable for graphs that are not very large, and an option that optimizes performance by evaluating only a subset of neighbors. In the latter, using a large user-defined threshold ϵ probability, if the number of neighbors is greater than a 2nd user-defined threshold t2, a 2nd optimization of cutting down the number of neighbors is applied before evaluating each of the neighbors for choosing the greedy neighbor, as follows. Here, the t2 highest neighbors are chosen for evaluation, where the order is decided by sorting the neighbors in descending order based on their maximum edge weight . Note how the first threshold t1 ensures that the sorting complexity O(t1log(t1))does not blow up.In the non-exhaustive search case, in the event of 1-O(1) lookup of the maximum edge weight of a neighbor, we store the neighbors of the subgraph as a hash map, where looking up a neighbor yields its maximum edge weight. This hash map also stores, for each neighbor, a list of edges connecting it to the subgraph and was constructed efficiently when the neighbors of each of the subgraph nodes were read from the corresponding file. After selecting the neighbor to add to the subgraph in the current iteration, this hash map is also used to efficiently add the neighbor to the subgraph by providing constant-time lookup to the edges that need to be added.Note that for efficient constant-time ϵ-greedy heuristic, we also have a simple base heuristic option, termed greedy edge weight, where we add the neighbor with the highest maximum weight edge at each step of the iteration. Note that since the ML model is not used at each stage of the growth, this is fast enough and does not require the optimization steps used in the ϵ- greedy approach where subsets of neighbors were selected for evaluation by the community fitness function.Instead of the base ϵ-greedy heuristic. Based on the scores of the current and previous iterations of the subgraph, we accept or reject the latest node addition using the user-defined heuristic - iterative simulated annealing (ISA), or a variant of ISA, termed pseudo-metropolis in which the acceptance probability  = k. In ISA, at each stage of growth of the current subgraph, its maximum scoring neighbor is added, except in the case when the new community score of the subgraph Snew is lesser than Sold, the value before adding the new node (i.e. Snew < Sold). In this case, the new node addition is accepted with a probability of,T0 and α, we update the temperature as T ← αT after every iteration.For both base heuristics, in any iteration, if no neighbors for the subgraph exist, the growth process terminates. If the community score of the subgraph in any iteration is less than 0.5, the node last added is removed and the growth process terminates. We provide additional heuristics that can be applied on top of the base bability  is a conWhen ISA or pseudo-metropolis heuristics are applied, we also evaluate an additional heuristic where the algorithm terminates if it has been 10 (or can be user-defined) number of iterations since the score of the subgraph has increased.ϵ-greedy, ϵ-greedy + ISA and ϵ-greedy + pseudo-metropolis. In all options, the algorithm terminates after a number of steps equal to a user-specified threshold. The default threshold provided is the maximum size of the known communities, and we also provide a smart option for when a few communities have a large number of nodes, where it is set to choose the maximum size after ignoring outliers. This number can also be improved by visual inspection of a boxplot of community sizes that is generated. Future work can also explore greedy edge weight + ISA and greedy edge weight + pseudo-metropolis heuristic algorithms and observe their performance. Note how there are 2 possibilities for exploration in the 3 algorithms other than the greedy edge weight heuristic algorithm. In the 1st stage, we pick a neighbor at random with low probability. In the 2nd stage, we accept the neighbor we picked in the 1st stage with low probability, if it yields a lower score than the original subgraph.In the implementation, we provide four options to the user - greedy edge weight,Communities with only 2 nodes are removed. Note that communities with 2 nodes are rarely found, and while dimers are biologically valid, since they do not have topological variation, we do not consider them in this work focused on higher order assemblies with different topologies as a key feature. We then merge communities that have a Jaccard similarity greater than a specified overlap threshold employing the merging algorithm discussed in the data cleaning section. The only difference is while merging, for two overlapping communities, the final community retained out of the 2 communities or the merged variant is the one that obtains the highest score with the community fitness function. In another variant of the merging algorithm, instead of the Jaccard similarity threshold , we use ϵ in the ϵ-greedy heuristic, k in pseudo-metropolis, T0 and α in iterative simulated annealing, and the overlapping threshold in the post-processing step are varied in parameter sweeps to select the best ones that work using the Qi et al F1 score  similar size distributions for both sets, and (iii) 70–30 ratios in train-test sets. Similarly, a minimal number of merges is attempted in the merging algorithms devised to maintain a high number of learned protein complexes. Further, unlike existing supervised methods, which evaluate the performance of their algorithms on a reduced network with only nodes present in known communities, we evaluate our algorithm on the full network for more accurate evaluation. Finally, we note that Super.Complex uses only topological features of networks, and can be applied to community detection on networks from various fields, with the possibility of including domain-specific features to learn more accurate domain-specific community fitness functions. Our methods are also applicable in domains with limited or no knowledge by transferring community fitness functions from other domains, such as the defaults we provide for human protein complex detection.Since the number of known communities can be limited, we emphasize the preservation of known communities when splitting them into train-test sets while also ensuring (i) independence - Comparing sets of learned and known communities accurately is an outstanding issue. Poor evaluation measures do not satisfactorily identify the quality of learned communities and make it difficult to evaluate a community detection algorithm. Sets of learned communities achieving high scores with existing evaluation measures have been observed to have a lot of redundancies, e.g. multiple learned communities are very similar with high overlaps . Known bi.e. if we learn multiple same or very similar communities which are each individually high scoring, we will get a high value of precision-like measures. This is because in many cases, many to one matches are being made between learned communities and known communities. To deal with such issues, it is best to make one-to-one matches. The MMR  is one such good measure, however, it only calculates a recall-like measure by dividing the sum of the weights of edges  by the total number of known communities. Taken alone this cannot account for precision, for instance, if we learn a series of random subgraphs, these have low weights and will be ignored, while a high MMR score can be obtained from only a small number of high quality learned communities. Therefore, we define the precision equivalent for MMR, PFFM in An issue with many measures such as Qi et al F1 score  and SPA M is a set of weights of a set of maximal one-to-one matches, found using Karp’s algorithm [PFFM and recall RFFM, also shown in In lgorithm . The weiBy doing a one-to-one match, we are also indirectly penalizing cases where the benchmark community is split into multiple smaller communities in the learned set of communities, since the measure considers the weight of only one of the smaller learned communities that comprise the known community, ignoring the rest. Thus only the small weight of the matched community is considered, penalizing this case, unlike one-to-many measures that aggregate the contributions from each of the smaller communities to finally achieve a high score.i.e. an F-similarity score, by comparing the nodes of a learned and a known community. Our F-similarity score is a combination of the recall (of the nodes of the known community) and the precision (of the nodes of the learned community), as shown in  compute Our F-similarity score can be compared with a threshold to determine a match, and then the overall precision, recall, and F1 scores for the set of predictions can be computed as in i.e. as long as there exists a possible match, it is used as a 1 or 0 count towards the aggregate precision or recall measures. Having a measure that provides matches between learned and known communities allows easy identification of previously unknown communities. One to many matches such as Qi et al precision-recall (PR) measures that do not use an explicit matching between learned and known communities can be modified to obtain a matching. In the modified measure, for each community, we choose the most similar community in the other set in order to give the matching. While measures that use a threshold such as Qi et al F1 score  is a similarity measure between communities C1 and C2, with |C1| is the number of nodes in C1. Ck is a known community from K, the set of known communities and Cl is a learned community from L, the set of learned communities.In the Qi et al measures from , S1 Fil–8, a binF1 score  have theF1 score , the neiF1 score  can be uDifferent similarity measures –5, such i.e. recalls most of the big community and does not recall much of any of the smaller communities. Rather than inducing bias into a measure that decides which communities should be weighted higher, it may be a better idea to have a measure that gives equal weights to all communities. We define an unbiased sensitivity Snu in Consider the precision-like positive-predictive value (PPV), recall-like Sensitivity (Sn), and their combined Sn-PPV accuracy (SPA) , also giPPVu in PPVu and Snu as shown in In PPV, the denominator sums, for each learned community, the sum of the subset of nodes in the learned community shared by all known communities. This does not contribute accurately to a precision-like measure, as nodes that are absent in known communities are ignored. For instance, a learned community that has all the nodes in a known community, but also includes a lot of possibly spurious nodes will be scored in the same way as a learned community which is an exact match to the known community. Further, in PPV, nodes in a learned community shared by multiple known communities get counted an extra number of times in the denominator. So if we share a set of nodes with multiple known communities we get penalized more than (i) if we share the set with only a few known communities, or (ii) if nodes of our community are shared with different known communities in a disjoint manner. The reasoning for allowing such behavior is again biased and does not support the detection of overlapping known communities. For example, a learned community that has a high overlap with 2 known communities  will contribute lesser (0.5) to a PPV score than a learned community which overlaps lesser with one known community . To overcome these issues, we propose an unbiased PPV, i.e. low numbers of very similar overlapping learned complexes. The clique-based measures from [From the sensitivity of measures plot in res from  - F-granWe first test and ensure that the pipeline achieves perfect results on a toy dataset we construct comprising disconnected cliques of varying sizes, each corresponding to a known community, where we use all nodes as seeds for growth during the prediction step.http://hu1.proteincomplexes.org/static/downloads/pairsWprob.txt) has 7778 nodes and 56,712 edges, after an edge weight cutoff of 0.0025 was applied to the original 64,048 edges. There are 188 complexes after data cleaning, a set we term as ‘refined CORUM’, out of the original 2916 human CORUM complexes, which underscores the importance of minimizing any losses in the merging and splitting steps of the pipeline. In the data cleaning process, overlapping complexes with a Jaccard coefficient j greater than 0.6 are removed, as this value was used in the experiments of hu.MAP 2-stage clustering. Note that of the complexes from CORUM that were removed, there were over 1000 complexes that had fewer than 3 members, and the remaining removed complexes consisted of duplicates and disconnected complexes with edges from hu.MAP. Note, however, that hu.MAP was the highest confidence human protein interaction network integrating 3 large previous human protein-interaction networks, all built using high confidence data from large-scale (~9000) laboratory experiments. The edge weights of hu.MAP were trained using an SVM based on features obtained from experiments.To learn potentially new human protein complexes, we apply Super.Complex on the human PPI network hu.MAP  using a The best results, following different parameter sweeps from the experiment on hu.MAP are given in We use i.e. the criteria of a score less than 0.5 or no observed score improvement over a specified number of steps .In Evaluation measures comparing learned complexes on hu.MAP by Super.Complex w.r.t known complexes from CORUM are given in To compare our method with published results from existing methods, we perform experiments on the data used by these methods in their experiments - a yeast PPI network, DIP - Database of Interacting Proteins  with kno1028 learned human protein complexes by Super.Complex, along with refined and original CORUM complexes as a resource on https://sites.google.com/view/supercomplex/super-complex-v3-0. The high precision values obtained by Super.Complex in We provide interactive lists and visualizations of the https://meghanapalukuri.github.io/Complexes/* where * is Protein2complex_annotated.html and Complex2proteins_annotated.html). Three examples of uncharacterized proteins  along with their corresponding complexes are highlighted in Fig 6. C11orf42 could potentially be related to trafficking, as it is a part of a complex with 30% similarity to the retromer complex, (i.e. with 0.3 Jaccard similarity to the known CORUM retromer complex), with additional evidence available from the Human Protein Atlas (HPA) [http://www.proteinatlas.org) showing subcellular localization to vesicles, similar to other proteins of the complex. C18orf21 also has evidence from HPA, localized to the nucleoli and interacting with other proteins of a complex with 50% similarity to the Rnase/Mrp complex with most members in the nucleoli/nucleoplasm. Further evidence from [111 uncharacterized proteins (Uniprot  annotatias (HPA)  (availabnce from  also indSupplement 1S1 File. Document containing supplementary tables, results and methods. a. Table 1. Comparing Super.Complex with 2-stage clustering on the hu.MAP dataset using 6 existing and 3 new evaluation metrics shows comparable performance for both algorithms.Results: Algorithm Guarantees, Robustness of the Super.Complex algorithm, Performance, SOTA Availability, and SARS-CoV-2 affected protein complexes.b. Methods: Topological Features, Similarity measures for evaluation, Evaluation with existing measures, Time complexityc. S1 Fig. SARS-CoV-2 - human protein complex map showing complexes identified by Super.Complex. a. A section of the full map, featuring SARS-CoV-2 nsp4 and orf6 and their interacting human protein complexes b. A protein complex with a 30% match to the Nup 107–160 subcomplex interacts with both SARS-CoV-2 nsp4 and orf6 c. Map of SARS-CoV-2 nsp2 interactions with human proteins and their corresponding complexes d. A complex with a 20% match to the Endosomal targeting complex, and e. A complex with a 40% match to the retromer complex, both of which interact with SARS-CoV-2 nsp2. An interactive map is available at https://meghanapalukuri.github.io/Complexes/SARS_COV2_Map_only_mapped_complexes_names.html.


Streptococcus mutans and Lactobacillus acidophilus are facultative anaerobic bacteria that are found in tooth decay. Accordingly, neem leaf extract was developed due to its great anti-microbial property against many bacteria. The aim of this study was to determine anti-cariogenic properties of neem leaf extract in a novel paste preparation. Dental caries is a major oral health problem, which associates with cariogenic bacteria. S. mutans and L. acidophilus test were initiated at bacterial concentration of 108 CFU/ml. The antibacterial activity was then performed by disc diffusion method following by minimum bactericidal concentration (MBC) technique.The neem extract was derived from maceration of dry neem leaves in ethanol for 48 h. The ethanolic extract was subjected to chemical identification using GC-MS. Neem pastes were prepared from ethanolic extract mixed with polyethylene glycol paste with or without zinc oxide. S. mutans growth. Neem pastes and ethanolic extract at concentration of neem 6.25 mg/ml inhibited L. acidophilus growth. GC-MS result displayed 35 compounds. Compounds found in the extract were n-Hexadecanoic acid (31.18%), Hentriacontane (18%), Phytol (16.79%). Disc diffusion showed that ethanolic extract and neem pastes inhibited growth of both bacteria. For MBC, neem paste with zinc oxide at concentration of neem 0.4 mg/ml was the most effective concentration on inhibiting S. mutans and L. acidophilus. By this property, neem paste could be developed for the application in dental field, i.e. pulp capping.The ethanolic neem leaf extract and novel neem pastes had antimicrobial effect on both  Key words:Neem, Azadirachta indica, antimicrobial, cariogenic bacteria. Dental caries is a health issue affecting people worldwide. It has been estimated in Asian population around 52.6 % in primary teeth and 58.8% in permanent teeth . Dental A technique in dental restorations, called pulp capping, is used to prevent the dental pulp from necrosis after being exposed or nearly exposed during a cavity preparation. This technique is beneficial for the success of vital pulp therapy because it can protect the pulp against external irritants such as microbial challenges, resulting in a long-term survival of pulp tissue ,5. One oAzadirachta indica) is typically grown in tropical and semi-tropical regions such as in Thailand and India. Its leaf, bark, and seed are used for therapeutic purposes. Numerous biological and pharmacological activities of neem leaf has been reported including antibacterial . GC-MS was performed at the Office of Advanced Science and Technology, Thammasat University. Ethanolic extract was subject to GC by 7890B, Agilent technology, USA, and MS by 55977A, Agilent technology, USA, auto-sampler model. For GC, helium was used as carrier gas at a constant flow of 1 ml/min through HP-Innowax column. An injection volume of 1.0 µl was employed (split ratio of 20:1) injector at a temperature of 250°C. The temperature was programmed with an increase of 8°C/min from 50°C to 250°C, and then held for 15 min at 250°C. For MS, the electron impact was run at 70 eV with full scan mode and at mass range 45-700 m/z. at 40 min.-Paste preparationSix paste forms were prepared. Each preparation contained different composition of zinc oxide and concentration of neem extract. The paste base included polyethylene glycol, stearyl alcohol, glycerol, sodium lauryl sulfate, and distilled water. Paste 1 (P1) was paste base without zinc oxide. Paste 2 (P2) was 25 mg/g neem in paste base without zinc oxide. Paste 3 (P3) was 100 mg/g neem in paste base without zinc oxide. Paste 4 (P4) was paste base with zinc oxide. Paste 5 (P5) was 25 mg/g neem in paste base with zinc oxide. Paste 6 (P6) was 100 mg/g neem in paste base with zinc oxide. Pastes were prepared to a final concentration of 20 mg/ml in water. Then, pH of the preparations was evaluated using a pH meter (Thermo Sciencetific Orion Versastar pro). The measurement was carried out in triplicates.-Antibacterial activity•Disc diffusionS. mutans (ATCC25175) in Müeller-Hinton broth at 0.5-0.7 OD using an Ultraspec 2000 spectrophotometer at 600 nm were used in this experiment (108 CFU/ml). The 200 µl of bacteria suspension was placed on Mitis salivarius agar surface. The L. acidophilus (ATCC4356) in de Man Rogosa and Sharpe (MRS) broth at the similar OD and volume also used and placed in MRS agar surface. The methanolic extract, ethanolic extract, aqueous extract (25 mg/ml), 0.2% chlorhexidine, Ca(OH2) paste, 0.9% Normal saline (NSS), P1, P2 , P4 and P5 were examined by immersing 6 mm paper disc into 30 µl of solution or 1.5 mg of paste. Then, they were incubated with gas pack at 37°C for 48 h. The diameter of inhibition zones was measured and recorded in centimetre (cm). The experiments were done in 5 independent experiments and each experiment was done in triplicate.The study of antimicrobial effect of the extractions and neem paste using agar disc diffusion method. The •Minimum Bactericidal Concentration (MBC) The concentrations of P1 and P4 were five-fold serial dilutions ranging from 1.6 to 1000 mg/ml. The ethanolic extract was two-fold serial dilutions ranging from 0.2 to 25 mg/ml. P3 and P6 were two-fold serial dilutions concentration of neem ranging from 0.4 to 6.25 mg/ml. The 100 μl of each solution was placed into a sterile 96-well microtiter plates. The 100 μl of bacterial inoculum (108 CFU/ml) were added to each well. The covered microtiter plates were incubated with gas pack for 24 h at 37°C. Then, 100 µl aliquot from wells were cultured on 60×15 mm nutrient agar plates to determine the bactericidal activity, and incubated with gas pack for 48 h at 37°C.-Statistical analysisp<0.01, *** p<0.001 vs. 0.2% chlorhexidine, # p<0.05, ## p<0.01, ### p<0.001 vs NSS, and 0 p<0.05, 00 p<0.01, 000 p<0.001 vs Ca(OH)2.All data was determined for normal distribution using Kolmogorov–Smirnov normality test. The differences among groups were calculated by One-way ANOVA with Tukey’s multiple comparison test. The data was presented by means ± SE. Significance was assigned as *p<0.05, ** -GC-MSGC-MS result displayed 35 compounds. n-Hexadecanoic acid was most be found in this extract at RT 25.771 (31.18%), Hentriacontane at RT 25.63 (18%), Phytol at RT 22.961 (16.79%), N-methyl-N-(methyl-d3) aminoheptane at RT 19.922 (3.2%), Neophytadine at RT 15.224 (2.87%), alpha-Copaene at RT 9.169 (3.34%), alpha-cubebene at RT 8.63 (1.87%), alpha.-Humulene at RT 11.83 (1.7%), and other miscellaneous compounds (15.05%), respectively , normal saline (NSS) and Ca(OH)2 were served as positive control, negative control, standard treatment, respectively. All three extracts showed significantly higher inhibition zone than NSS and Ca(OH)2. The ethanolic extract yield a better inhibitory effect by up to 2.3-fold of Ca(OH)2. When compared with chlorhexidine, all extracts were lower in inhibition zone. There was no significant difference among all three extracts tested 2. Furthermore, ethanolic extract was able to increase inhibition diameter more than P1, NSS and Ca(OH)2. No significant difference was observed among ethanolic extract, P2, P4, and P5. However, the effect of all samples was not on the level of chlorhexidine  can decrease the level of bacterial counts in vivo . Dodecanin vitro  and restin vitro .Other medicinal herbs that have anti-microbial activity also contain substances found in neem extract. This includes such as β-Bourbonene , β-copaeS. mutans and L. acidophilus. This was synergistic effect between constituents in paste and ethanolic extract. The antimicrobial effect could be later applied in pulp capping material.The neem paste had antimicrobial effect on


This work provides a route to intercalate OMMT interlayer with high gas barrier polymers and thus can be a useful reference to fabricate PLA/OMMT composites with improved gas barrier and mechanical properties. A comparison of oxygen permeabilities with existing commercial packaging films indicates that the biodegradable PLA/PVA/OMMT may serve as a viable substitute for packaging film applications.Polymer/clay composites are an innovative class of materials. In this study, we present a facile method for the preparation of biodegradable and robust PLA/organomodified montmorillonite (OMMT) composite films with excellent gas barrier performance. When the design of PLA/OMMT composite films, in addition to making OMMT have good intercalation effect in the matrix, the compatibility of intercalating polymer and matrix should also be considered. In this work, two polymers with high gas barrier properties, namely poly (PVA) and ethylene vinyl alcohol copolymer (EVOH), were selected to intercalate OMMT. The morphology and microstructures of the prepared PLA/PVA/OMMT and PLA/EVOH/OMMT composites were characterized by the X-ray diffraction measurement, scanning electron microscopy, and differential scanning calorimetry. It was shown that the good dispersibility of PVA in the PLA matrix, rather than the intercalation effect, was responsible for the improved gas barrier and mechanical properties of PLA/PVA/OMMT composite. The elongation at break increases from 4.5% to 22.7% when 1 wt % PVA is added to PLA/OMMT. Moreover, gas barrier of PLA/PVA1/OMMT measured as O The shelf life of food is very important for maintaining its freshness and quality . Studies2 [Polylactic acid (PLA) is one of the most promising alternatives to petroleum-based polymers in the food packaging industry, owing to its excellent mechanical robustness , renewab2 . As PLA 2 , it repr2 .Montmorillonite (MMT) is one of the most widely used nanoclay. MMT behaves as a smectic liquid crystal, which means that it forms oriented planes that stack and slide one over another in the crystal. This property further allows significant expansion of interlayer space or swelling. When combined in various polymer–MMT composites, it enhances the performances of pure polymers by increasing the interfacial area between the polymer matrix and clay platelets due to the exfoliation of crystalline clay bundles.Considering food packaging applications, enhanced mechanical properties , decreasIn this work, two polymers with high gas barrier properties, namely PVA and ethylene vinyl alcohol copolymer (EVOH), were intercalated between OMMT layers to obtain robust and biodegradable composite films. The effect of PVA and EVOH addition on the intercalation and dispersibility of OMMT in PLA along with mechanical and gas barrier properties of PLA/OMMT composites were studied in detail. The chemical structures of PLA, PVA and EVOH are shown in D-LA was supplied by NatureWorks . The OMMT (I.24 TL), with a length/thickness ratio of 80 where the organic modified is 18-amino stearic acid, was purchased from Nanocor Inc. . Before processing, OMMT and PLA were oven-dried at 80 °C for 12 h. PVA (17–88) with a degree of polymerization 1700, and alcohol content of 88% was obtained from Aladdin. Ethylene vinyl alcohol copolymer (EVOH) resins supplied by Kuraray Co., Ltd., Tokyo, Japan were H171 grade and contain 38 mol% ethylene with the density of 1.17 g cm−3 and a melt flow index of 1.8 g/10 min at 190 °C. PVA and EVOH granules were oven-dried at 80 °C for 12 h before internal mixing with PLA and OMMT.The PLA  with approx. 2% The composite plates were prepared using the internal mixer (Haake Rheometer) operating at 180 °C for 5 min with a 60 rpm rotation speed. Next, the composite material was granulated using a crusher. After that, it was molded using a hydraulic press operating at 10 MPa and 180 °C to produce 80–90 µm thick films. The composition of different nanocomposite films prepared in this work is listed in χc) according to Equation (1).w is the weight percentage of PLA in the composites.The X-ray diffraction (XRD) patterns of all samples were recorded on a Bruker D8 Advance X-ray diffractometer (Germany), which uses a cooper radiation source operating at 40 kV and 40 mA. The scan speed was set to 0.5°/min between 2° and 10° and 2°/min from 10° to 40°. The scanning electron microscope (SEM) analysis of the freeze-fractured composite samples was done on the Phenom Pro machine (Netherlands) operating at 10 kV. Differential scanning calorimeter (DSC) experiments were conducted on a TA Q20 instrument to determine the thermal properties of fresh polymer pellets. The sample was heated in an inert atmosphere obtained by passing the nitrogen gas at 20 mL/min flow rate to 190 °C at a heating rate of 20 °C/min. This temperature was maintained for 5 min, and then the sample was cooled to 30 °C; this temperature was kept for 3 min, and the final heating run proceeded with 10 °C/min rate until reaching 190 °C. The integration of melting peaks provided the melting enthalpies, which were then used for the calculation of the degree of crystallinity (3.7 J/g) ; and w i2 film was measured using an oxygen transmission rate tester 31M  at 23 °C and 30% humidity. The reported values represent the average of three independent measurements. The tensile strength (TS) and the elongation at break (EB) of the composites was analyzed according to GB/T 1040 standard, applying a tensile rate of 5 mm/min via electric tensile tester . The final data are the average of 10–15 specimens for each sample.The gas permeation properties of PLA/OMMT nanocomposite films were analyzed following ASTM D3985 standard . The oxyθ values between 2–10° correspond to OMMT in the nanocomposites, whereas neat PLA and the PLA in the nanocomposites exhibit XRD peaks between 10–40°. The observed 2θ values and the interlayer d-spacing calculated from Bragg’s law are listed in The changes in the intercalation, dispersion of OMMT, and crystal structure of PLA upon the addition of PVA and EVOH to PLA/OMMT system were studied by XRD. Previously, we found that PLA/OMMT nanocomposites with 6 wt % OMMT show the highest dispersibility and the best gas barrier performance , so in t2)17NH3+Cl−), which adsorbs on the internal surface of interlayer space. In general, the enhancement in the barrier properties of polymer/OMMT composite films is mainly dependent on the dispersion level of OMMT lamella in the polymer matrix. Two factors affect the dispersion of OMMT in the PLA matrix, namely: (1) the fabrication method used for the preparation of polymer/OMMT composites and (2) the interactions between the OMMT surface and the polymer chains. The melt mixing method used in this work was considered to be a better method than the solution mixing technique because the dispersion of OMMT sheets is favored under strong shear stress [In this work, the structure of MMT was modified with the addition of polar, long-chain 18-amino stearic acid (HOOC(CHr stress . Furtherθ = 5.22° assigned to (001) plane, giving the interlayer spacing of 1.69 nm. The most intense peak of neat PLA is observed at 2θ = 16.4° and assigned to the diffractions from (110)/(200) planes. As shown in According to our previous work , pure OMd-spacing. All PLA/PVA/OMMT samples show higher interlayer spacing than PLA/OMMT, which proves that the addition of PVA improves the intercalation effect of OMMT in PLA. Interestingly, a new diffraction peak at 2θ = 6.98° appeared in the XRD spectrum of the sample with 3 wt % PVA, which is explained by the stacking of a portion of layers leading to their aggregation [d-spacing of OMMT layers similarly to PVA. On the other hand, there was no peak at 2θ = 6.98° in PLA/EVOH/OMMT samples, indicating weaker stacking interactions between the layers and better intercalation effect of EVOH on OMMT compared with PVA [1/2 cm3/2, respectively [1/2 cm3/2 [1/2 cm3/2) compared with PVA–OMMT pair (2.6 J1/2 cm3/2) indicates stronger interactions of OMMT and EVOH polymer.The intense (001) peak typical of OMMT is observed in regation . Figure with PVA . Somewhawith PVA , where cectively ,31,32, a/2 cm3/2 . The smaIn addition to the degree of OMMT intercalation, the gas barrier performance of PLA/PVA/OMMT and PLA/EVOH/OMMT is also related to the compatibility of intercalated polymer (PVA and EVOH) with the PLA matrix. Poor compatibility leads to structure defects at the interface or the agglomeration of intercalated polymer. The dispersion of PVA or EVOH in the PLA matrix was studied by SEM. Smooth surfaces observed for PLA and PLA/OMMT fractured films a,b suggeTg of PLA in the PLA/PVA/OMMT and PLA/EVOH/OMMT composites were lower than those of neat PLA (Tcc) of PLA were somewhat lower in nanocomposites than in neat PLA, as PVA and EVOH chains could serve as nucleation centers to promote crystallization and reduce Tcc.The DSC curves suggest that the neat PLA . This imTcc and also influences the crystallinity (χc) of PLA matrix. The addition of 1 wt % PVA increases the crystallinity of the matrix from 3.1% to 7.0% and decreases Tcc by 4.1 °C. In contrast, further addition of PVA reduces χc from 7.0% to 1.7% and shifts Tcc toward higher temperatures. These results suggest that there is a certain amount of PVA for optimum cold crystallization effect, while the excessive PVA content reduces the flexibility of PLA chains. This further results in the reduced compatibility between two phases and the decreased extent of heterogeneous nucleation. Tcc and χc of PLA matrix upon the addition of EVOH. The same trends were observed for PVA, so there is also an optimum level of EVOH for heterogeneous crystallization, above which EVOH agglomerates and hinders the movement of PLA chains.PO2). The results are listed in 2 when the amount of PVA exceeded 1%. Poor dispersion of PVA in PLA matrix previously observed at higher PVA levels results in structural defects of the polymer film. When the amount of intercalated polymer is low, it occupies interstitial space and spreads homogeneously all over the matrix, which enhances the gas barrier properties of composites. Higher amount of PVA decreases compatibility between PVA and PLA matrix, leading to agglomeration, crystal structure defects, and other issues that reduce gas barrier properties.The gas barrier properties of composite films were characterized by the measurement of oxygen permeability (Next, we compared the mechanical and gas barrier properties of PLA/PVA1/OMMT film with several commercial packaging materials. The results presented in In this work, we demonstrated a facile procedure for the preparation of PLA/OMMT nanocomposite films with excellent gas barrier properties and good mechanical robustness. It was shown that the design of composite films should consider the structure of OMMT lamella and intercalating polymer as well as compatibility between intercalating polymer and polymer matrix. Although the intercalation effect of PVA was not as good as that of EVOH, PVA had excellent dispersion in PLA matrix that resulted in better mechanical and gas barrier properties of PLA/PVA1/OMMT compared to PLA/EVOH1/OMMT composite film. The EB increased from 4.5% for PLA/OMMT to 22.7% for PLA/PVA1/OMMT, whereas oxygen permeability was decreased by 52.8% when comparing PLA/PVA1/OMMT with neat PLA film. Therefore, this work provides a route to intercalate OMMT interlayer with high gas barrier polymers and, thus, can be a useful reference to fabricate PLA/OMMT composites with improved gas barrier and mechanical properties. A comparison of oxygen permeabilities with existing commercial packaging films indicates that the biodegradable PLA/PVA/OMMT may serve as a viable substitute for packaging film applications.


Graphene combines high conductivity (sheet resistance down to a few hundred Ω/sq and even less) with high transparency (>90%) and thus exhibits a huge application potential as a transparent conductive electrode in gallium nitride (GaN)-based light-emitting diodes (LEDs), being an economical alternative to common indium-based solutions. Here, we present an overview of the state-of-the-art graphene-based transparent conductive electrodes in GaN-based LEDs. The focus is placed on the manufacturing progress and the resulting properties of the fabricated devices. Transferred as well as directly grown graphene layers are considered. We discuss the impact of graphene-based transparent conductive electrodes on current spreading and contact resistance, and reveal future challenges and perspectives on the use of graphene in GaN-based LEDs. GaN-based light-emitting diodes (LEDs) have been intensively studied for applications in the visible and ultraviolet (UV) spectral range ,2,3,4. G0.7Ga0.3N [Since the commonly used p-type cladding layers in GaN-based LEDs, such as Mg-doped GaN or AlGaN, have deep acceptors with an activation energy of, e.g., around 170 meV in GaN  or aroun.7Ga0.3N , the p-c.7Ga0.3N ,10. As a.7Ga0.3N ,12,13,14.7Ga0.3N . In the .7Ga0.3N . In the .7Ga0.3N . However.7Ga0.3N . Moreove.7Ga0.3N ,21 restr.7Ga0.3N  could le.7Ga0.3N . Additio.7Ga0.3N .2-hybridized carbon atoms with a single-atom-thickness, can exhibit a high transparency of >91% in a wide range from 200 nm to 1000 nm [2 [Graphene, defined as a two-dimensional (2D) carbon material consisting of a hexagonal array of sp 1000 nm , a low s250 Ω/sq ,27,28 th250 Ω/sq ,30 has m250 Ω/sq ,32. Base Ω/sq [2 ,34,35, w Ω/sq [2 ,37,38 an Ω/sq [2 . This miIn the second decade of the 21st century, GaN-based LEDs with graphene as TCSL have been intensively studied. Different aspects of this research are reviewed in this work. The main efforts of these studies focused on the reduction of the contact resistance, and in keeping the low sheet resistance and high transparency of graphene-based TCSL after integration into GaN-based LEDs. In this review, the numerous efforts for integration of graphene-based TCSL in GaN-based LEDs with single- (SQW) or multi-quantum wells (MQW) are summarized. In In this section we will present the efforts of integrating graphene either alone or in a hybrid structure serving as TCSL in GaN-based LEDs.Here, the performance of a TCSL of graphene fabricated by either transfer or direct growth process is discussed. The fabrication details of transferred and transfer-free graphene films are reviewed in other works ,41,42 anAs previously mentioned, single layer graphene (SLG) has a high theoretical (~97%) and experimental (≥91%) optical transmittance   and UV ((460 nm)  emittinge, 3.4 V . Many ste, 3.4 V  or with e, 3.4 V  or a lowe, 3.4 V ,52  , 30–48 Ω 240 nm) ,54 and 2 280 nm) , respect 280 nm) ,44,52 an 280 nm) ,54,55,56 280 nm) . However 280 nm)  and chem 280 nm) .2 [As summarized in 2 . This di2  to 4.5 e2 , which i2 ,62. This2  or micro2  that det−3 Ωcm2, respectively. Both could be a result of the weakly controlled transfer process.Despite the apparent potential of graphene as a TCSL, device performance in GaN-based LEDs with transferred graphene as the TCSL is not yet convincing. This might be attributed to the unexpected high sheet resistance and the extremely high contact resistance at the graphene/p-GaN interface, both of which deviate widely from the theoretical value of several tens of Ω/sq and 10Graphene directly grown on the p-side of GaN-based LEDs is expected to solve problems originating from the transfer process, such as bad adhesion, polymer residuals, defects and other damage introduced during transfer. Three main methods for direct integration of graphene layers as TCSL into GaN-based LED are reported: spray deposition of a graphene dispersion solution with a subsequent heating treatment  ,63, cataAs illustrated in sed LEDs . It is a2 is still higher than the value of ITO/p-GaN; however, it is better than typical 2 of transferred graphene on p-GaN [Simulation results suggest that for a large current spreading length a TCSL with a low sheet resistance is essential . Howeveron p-GaN ,59,67. M2 as compared to transferred MLG leads to a reduced Most directly grown graphene TCSLs in GaN-based LEDs are multilayer, with a strong variation in optical transmission. In case of CCG-grown graphene the optical transmission in the spectral range between 200 nm and 800 nm is higher than 85%, whereas the sheet resistance of above 2 kΩ/sq leads to a higher ITO TCSL . Similarimproved . Only PEimproved  or witho UV LEDs , PECVD-g2 have not been achieved through either of the above-mentioned methods. The possible reasons are the relatively small grain size of graphene (~30 nm) and the high defect density, with distances between defects of around 2–5 nm [However, the expected values of d 2–5 nm , as repoTo achieve a better device performance, such as a lower As previously mentioned, both transferred and directly grown graphene TCSL on the p-GaN LED substrate have a higher sheet resistance, and a higher contact resistance to p-GaN, than ITO. In contrast to ITO with, for example, 10% transparency at 300 nm, the transmission of graphene TCSLs remains sufficiently high (>80%) in the UV-VIS spectral range as illustrated in As shown in The 3–10 nm thin ITO films can have a high transparency of ≥94.5% from 320 nm to 780 nm, which is even better than 3LG as listed in nly TCSL .−3 Ωcm2 [2 in graphene-only TCSLs integrated in LEDs  is normally formed by two steps. First, thin metal films were deposited between graphene and p-GaN or onto the graphene. Then the whole hybrid structure was exposed to a rapid thermal annealing step to form the nanoparticles. The thickness of the original metal films does not only define the dimension and density of nanoparticles  but also2  or even [−5 Ωcm2 . Additio[−5 Ωcm2 . Some UV[−5 Ωcm2  or SLG/A[−5 Ωcm2  as TCSL For the hybrid structure of graphene combined with nanowires (NWs), most studies integrate nanowires between graphene and p-GaN and sustain a high transmittance: 92.8% at 550 nm (FLG/Ag NW) , 86.3% aG/Ag NWs , while or of two  or by 75r of two  comparedr of two . As a rer of two ,78 and ar of two .In summary, graphene hybrid structures with metal thin films, nanowires or nanoparticles can reduce the sheet resistance and improve the contact property of TCSLs in GaN-based LEDs. However, the transmittance of the hybrid structure is kept above 80% only in nanowires and nanoparticles, whereas it is around 70% for a graphene/metal thin film hybrid as TCSL. This limits the improvement of LED performance.Different from the 2D graphene layer, two new types of graphene nanostructures, namely zero-dimensional (0D) graphene quantum nanodots (GQDs) and three-dimensional (3D) graphene foam, were also applied to enhance the GaN-based LED performance.The implementation of QNDs in a GaN-based LED device structure is illustrated schematically in A 3D foam structure of graphene, which has hierarchical macro- and meso-porous structures, was considered to be able to sustain a high electrical conductivity and a good mechanical stability , as welln quartz . Due to To describe the current spreading effect by inserting a TCSL, a current spreading length Ls=2n.05–1.35 . The sheAs outlined in A Schottky barrier is expected to form when graphene is in direct contact with p-GaN because of the work function mismatch. Meanwhile, graphene can be doped, leading to a change in its work function and thus of the Schottky barrier height (SBH) at the graphene/p-GaN interface. XPS results reveal an SBH at the graphene/p-GaN interface of 2.06 eV , which i−1 Ωcm2) is higher than the value of MLG/p-GaN (5.8−2 Ωcm2) [Some other factors can influence the SBH values as well, for example, the layer number of graphene. This has been shown for graphene deposited on n-GaN. As revealed by XPS, the binding energy position of the Ga 3d core level is 21.5 eV (bare GaN), 21.4 eV (SLG coated), 21.3 eV (2LG coated) and 21.1 eV (MLG coated). This core level shift impacted by the number of graphene layers is related to the Fermi level shift of graphene and thus reveals a change of the SBH at the graphene/n-GaN interface, i.e., the SBH of SLG/n-GaN is smaller than in case of MLG/n-GaN . In cont−2 Ωcm2) . Another−2 Ωcm2) . TherefoThe intentional p-type doping of graphene TCSLs applied in GaN-based LEDs can be divided into the noble-metal ion doping  and non-3 [3 and RhCl3 [3 concentration increases from 0 to 20 mM (1 mM = 10−3 mol/L). However, a further increase of the AuCl3 concentration to 30 mM will slightly reduce the graphene work function [Use of various metal-chlorides, such as AuCl3 ,73,93,94nd RhCl3 , was attfunction . The cur3+ is reduced to Au0 through electron depleting in graphene [0 atoms tend to form nanoparticles during this process, and the nanoparticle size increases as the dopant concentration increases [As revealed by XPS, the noble-metal-chloride doping process is a charge transfer process, where Augraphene ,96. Au0 ncreases . Howeverncreases .3, and further down to 158.5 Ω/sq (10 mM) and 112.4 Ω/sq (20 mM), respectively [3) with different metal ions show variations in the sheet resistance: The sheet resistance of undoped 4L graphene 3), 105 Ω/sq (IrCl3), and 140 Ω/sq (RhCl3), respectively, by doping.After doping, the sheet resistance of graphene layers can be reduced due to the added charge carriers in the graphene. Doping concentration and type of dopants can impact the final sheet resistance. With the same doping species, the initial value of ectively . For the2 originates from the SBH at the graphene/p-GaN interface and leads to a higher forward voltage. After AuCl3 doping, the work function of 12L graphene rises from 2 (undoped) to 0.4 Ωcm2 (p-type doped graphene), and the forward voltage from 6.59 V (undoped) to 5.55 V (p-type doped graphene) [2 and a forward voltage of 3 doping, 2 and 3.98 V, respectively. This is comparable to 3 doping level: at an injection voltage of 8 V, the injection current is increased by 48% (5 mM), 63% (10 mM) and 73% (20 mM), respectively in a GaN-based UV LED [Generally, the high contact resistance of around Ωcmraphene) ,73. ThisITO TCSL . The cur3 acid doping of graphene shows an enhancement of the current spreading in GaN-based LEDs as well [3 doping of MLG.One study with HNO as well . A signi3 doping in graphene layers has been illustrated. Pristine SLG fabricated by the roll-to-roll method shows over 91% transmittance from 200 nm to 1000 nm [3 doping, the maximal transmittance loss of graphene is only 5%, observed at 200 nm, while a high transmission of over 92% is sustained in a range of 300–1000 nm. This is highly promising for applying HNO3 doped graphene as TCSL in GaN-based UV-VIS LEDs as compared to metal chloride doped graphene, as in the latter the formed metal nanoparticles have generally a UV absorption  to 108 Ω/sq (HNO3 doped SLG) or from 40 Ω/sq (pristine 4LG) to 30 Ω/sq (HNO3 doped 4LG) [3 shows an increased work function from 4.52 eV to 5.31 eV as the HNO3 concentration is raised from 0 to 100 wt% [3 doping of transferred MLG.Due to some missing data in , such as 1000 nm . After Htion see . Similarped 4LG) . As meas 100 wt% . This inx, as a p-type metal oxide with a work function higher than 5 eV, is a most promising and well-investigated candidate for this application [x thin films can be >90% and >83% in the spectral range of 350–800 nm, respectively. Its current spreading performance is illustrated in x TCSLs and hybrid graphene/NiOx TCSLs are compared. NiOx is normally produced by oxidation of Ni thin films via annealing in air [2 containing ambient [x consists of NiO and Ni2O3 phases [Another effective method for modifying the transport barrier between graphene and p-GaN is introducing a thin metal oxide film with a high work function. NiOlication ,74,100. g in air ,35 or O2x stack can be increased. Taking FLG with x can be increased from around 80% to nearly 90% by reducing the Ni thickness from 2 nm to 1 nm [x/p-GaN, some works [x.By reducing the layer thickness of Ni, the transparency of the graphene/NiO to 1 nm . A post- to 1 nm . After tme works ,74,100 tx integrated in GaN-based LEDs are listed in x/p-GaN LEDs, a thermal annealing in N2 for 300 s brings the transmission from over 95% at 450 nm down to around 93.6% (RTA at 350 °C), 88.6% (RTA at 450 °C) and 87% (RTA at 550 °C), respectively [2+ or Ni3+ to Ni0, which indicates a metal phase formation in NiOx. Coincidently, the contact resistivity of the FLG/NiOx/p-GaN trilayer stack changes from 8.2−2 Ωcm initially to 5.6−2 Ωcm after RTA at 550 °C) [x/p-GaN with insertion of NiOx. Other works without RTA prove a direct reduction of the contact resistance at the p-side after introducing NiOx [x (1 nm or 2 nm) preserves the same sheet resistance value as the bare graphene, whereas the contact resistance decreases dramatically from 10−2−10−1 Ωcm2 in FLG/p-GaN to 5.9−4 Ωcm2 in FLG/NiOx/p-GaN. The roughly two orders of magnitude reduction in the contact resistance is attributed to changes in the band bending in p-GaN and a decrease of the contact barrier width due to the NiOx [Various performance data for graphene/NiOectively . This de 550 °C) . Therefoing NiOx ,35. The x insertion compared with the reference sample with bare graphene. However, for GaN-based blue LEDs, the LOP is still not as good as with ITO TCSL. Even though x/p-GaN is as low as 5.9−4 Ωcm2, the lowered x/p-GaN lower than in the case of a TCSL of a 280 nm thick ITO [Most works show an improvement of EL or LOP through NiOhick ITO . Therefo2O3 on graphene could further increase the LOP of GaN-based nanopillar LEDs by 30% as shown in Graphene TCSLs are also applied for GaN-based devices with complex 3D architecture, such as vertical light emitting nanorods, nanopillars and photonic crystal structures with air holes. Due to its mechanical robustness and stretchability ,67, grap2 substrate is 1000 Ω/sq but only 300 Ω/sq on the nanorods [2 to a triangle lattice air hole photonic crystal (PC) GaN-based LEDs [Transmittance and sheet resistance of transferred graphene in these advanced devices are similar to, or even better, than in the conventional planar device structures listed in nanorods . A similsed LEDs . A direc20 e2/h) . This hinanorods  and in pnanorods .Applying 3LG as TCSL, the GaN-based nanorod or nanopillar devices show 32% improvement of the LOP compared to planar structures and a higher LOP than the same nanorod structure with Ni/Au as TCSL . The GaNDifferent from the above architectures, horizontal core-shell GaN-based LEDs places n-GaN in the center of each pillar or pyramid, which is then surrounded by the MQW and p-GaN. This geometry can also provide strain relaxation, realize light emission in one unit and promise a reduction of the spectral broadening due to a well-organized growth of the nanowire size ,109. SinMonolithic GaN-LED arrays also require a highly transparent conductive layer as interconnection for the micro-LEDs to reduce the optical loss during light emission. Using graphene to connect p-GaN and n-GaN of two neighboring micro-LEDs, the LOP is 7.4% higher than in the same device structure with a Cr/Pt/Au metal interconnect . As a trGraphene with its unique properties is thought to be a promising material as a transparent current spreading layer in GaN-based LEDs for solving the current crowding issue due to the intrinsically low conductivity in the p-cladding layer. In the early development stage, sheet resistance and contact resistance of as-grown graphene were several orders of magnitude larger than conventional ITO, whereas the transparency of both materials in the visible spectral region was similar. The high sheet resistance in graphene films was attributed to the introduction of damage during the fabrication process, whereas the high contact resistance could originate from the intrinsic large work function difference between graphene and p-GaN, and bad adhesion and graphene damage by the transfer process.x. In case of metal doping, however, the formation of agglomerated metal nanoparticles could reduce light transmission. When inserting an ultrathin oxide layer such as NiOx between graphene and p-GaN, both stoichiometry and thickness of NiOx have to be well controlled to avoid a high transmission loss. All attempts to decrease the sheet resistance of graphene and the contact resistance on the p-side could deteriorate the transparency. Graphene TCSLs has been shown to be attractive even for new emerging devices with 3D architecture. However, systematic studies are still vacant, and a rational design strategy for the further development of graphene as TCSL in GaN-based LEDs is required.Graphene was modified in various ways in the past decade, aiming at improving the performance of GaN-based LEDs with graphene TCSL. To avoid defects, wrinkles and chemical residuals introduced from the transfer process, direct growth methods were developed. A reduction of the sheet resistance can be realized through hybrid structures, that network graphene with conductive participants in the form of layers, nanoparticles or nanowires. However, this enhancement typically results in a reduced transparency, which could weaken the LED performance. Improvements in the contact resistance at the graphene/p-GaN interface have been achieved through work function engineering of graphene through p-doping and through adding an ultrathin oxide layer such as NiO


Non-reversible fibrosis is common in various diseases such as chronic renal failure, liver cirrhosis, chronic pancreatitis, pulmonary fibrosis, rheumatoid arthritis and atherosclerosis. Transforming growth factor beta 1 (TGF-β1) is involved in virtually all types of fibrosis. We previously described the involvement of Ras GTPase isoforms in the regulation of TGF-β1-induced fibrosis. The guanine nucleotide exchange factor Son of Sevenless (Sos) is the main Ras activator, but the role of the ubiquitously expressed Sos1 in the development of fibrosis has not been studied. For this purpose, we isolated and cultured Sos1 knock-out (KO) mouse embryonic fibroblasts, the main extracellular matrix proteins (ECM)-producing cells, and we analyzed ECM synthesis, cell proliferation and migration in the absence of Sos1, as well as the role of the main Sos1-Ras effectors, Erk1/2 and Akt, in these processes. The absence of Sos1 increases collagen I expression , total collagen proteins, and slightly increases fibronectin expression; Sos1 regulates fibroblast proliferation through both PI3K-Akt and Raf-Erk pathways, and Sos1-PI3K-Akt signaling regulates fibroblast migration. These study shows that Sos1 regulates ECM synthesis and migration (through Ras-PI3K-Akt) and proliferation (through Ras-PI3K-Akt and Ras-Raf-Erk) in fibroblasts, and describe for the first time the role of the Sos1-Ras signaling axis in the regulation of cellular processes involved in the development of fibrosis. In most cases, when organs suffer injuries motivated by different disorders or diseases, a complex cascade of cellular and molecular responses triggering fibrosis of the tissue begins. When this phenomenon occurs over a prolonged period of time, this ends up causing irreversible parenchymal damage, cellular dysfunction and functional failure of the organ . This prin vivo model induced by ureteral obstruction in mice  synthesis, proliferation and migration in fibroblasts . We have in mice . Activat in mice .Son of Sevenless (Sos) proteins are the most widely expressed and functionally relevant family of Ras guanine nucleotide exchange factors (GEFs). There are 2 members in mammals, Sos1 (ubiquitously expressed) and Sos2 . Sos binMost of studies have been focused on identifying Sos1 functional roles, since the Sos2 isoform seems to be mostly dispensable . However2 in DMEM medium  supplemented with 10% fetal calf serum  and 100 U/ml penicillin/streptomycin. Cells were seeded in different plastic formats depending on the experiment to be carried out: 100 mm diameter Petri dishes for western blot and 24 well plates for proliferation studies and total collagen measurement. Fibroblasts, after achieving 10-20% (proliferation studies) or 70-80% confluence (studies on extracellular matrix proteins) and serum-starved for 24 h, were treated with human recombinant TGF-β1  or control vehicle during 24-48 h in serum-free medium. Pharmacological inhibition was performed 30 min before TGF-β1 stimulation with mitogen activated kinase/Erk kinase-1 (MEK-1) inhibitor U0126  or the PI3K inhibitor LY294002 . Mouse embryonic fibroblasts were subcultured and immortalized from Sos1-KO E13.5 embryos as previously reported . FibroblTotal cell number was measured by crystal violet staining as previously described . FibroblProtein expression was analyzed by western blot as previously described , using t3H]-proline  into collagen proteins was used to quantify collagen content in the culture medium, as previously described (3H]-Proline (specific activity: 40 Ci/mmol) for 24 h in fresh DMEM serum-free medium. Proteins were precipitated in ice-cold 10% trichloroacetic acid and the pellet was washed and resuspended in 0.1 N NaOH. [3H]-Proline incorporated into collagen proteins was measured in a Wallac 1409 DSA β liquid scintillation counter .The incorporation of [escribed . RadiolaIn vitro scratched wounds were created with a straight incision on serum-starved confluent cell monolayers with a sterile disposable pipette tip, as previously described . The Kolmogorov-Smirnov test was used to assess the normality of the data distribution. Comparison of means was performed by two way analysis of variance (ANOVA) and Bonferroni post-test. Statistical analysis was performed using Graph Pad Prism version 5.00 for Windows, Graph Pad Software, San Diego California United States, www.graphpad.com. A “p” value lower than 0.05 was considered statistically significant.Data are expressed as mean We analyzed the changes induced by the absence of Sos1 in the activation of two of the main intracellular pathways stimulated by Ras: rapidly accelerated fibrosarcoma (Raf)-mitogen-activated protein kinase (MAPK) cascade, whose final effect is the phosphorylation (on Tyr204) of extracellular signal-regulated kinases (Erk), and the phosphatidylinositol 3-kinases pathway (PI3K) that leads to phosphorylation (on Ser473) of Akt (protein kinase B). The absence of Sos1 does not affect the expression of phospho-Erk in basal conditions , the main cytokine involved in fibrotic processes and in the synthesis of ECM . TGF-β1 We assessed the synthesis of ECM proteins by analyzing collagen I and fibronectin expression, as well as total collagen synthesis. The absence of Sos1 induces increases in fibronectin expression , collageThe inhibition of Erk phosphorylation with U0126  does not affect the expression of fibronectin or collagen I either in the presence or absence of Sos1 . The inhCell proliferation is significantly lower in Sos1 KO fibroblasts compared to wild type fibroblasts in basal conditions (serum-depleted cells) as can be seen by a smaller increase in the number of viable cells  at 24 and 48 h . In addiIn the absence of Sos1, inhibition of Erk phosphorylation with U0126 significantly reduces cell proliferation, both at basal conditions and after TGF-β1 treatment. In addition, pre-treatment with U0126 also inhibits TGF-β1-induced proliferation in wild type fibroblasts. On the other hand, inhibition of Akt phosphorylation with LY294002 also significantly reduces cell proliferation, both at baseline conditions and after TGF-β1 treatment, regardless of the presence of Sos1 .All these data suggest that the absence of Sos1 affects fibroblast proliferation, and this process is regulated both by the PI3K-Akt and by the Raf-Erk pathway, the latter signaling pathway being dependent on the presence of Sos1.in vitro procedures: scratch time-course assay , total collagen proteins, and slightly increases fibronectin expression; the absence of Sos1 influences fibroblast proliferation through both PI3K-Akt and Raf-Erk pathways, and Sos1-PI3K-Akt is necessary for fibroblast migration.Sos1 and Sos2 isoforms show structural homology and similar expression patterns, but the specific functional properties of both isoforms are not fully known. The Sos2 isoform seems to be expendable, since adult Sos2-null mice are perfectly viable and fertile . HoweverH-ras–/–/N-ras–/– -inducible, conditional Sos1-null mutation . Our datN-ras–/– , H-ras–/H-ras–/– , N-ras–/N-ras–/–  and K-raroblasts  with resH-ras–/– , N-ras–/N-ras–/–  and in Kroblasts . The decroblasts .Our results suggest that the absence of Sos1 affects cell proliferation and migration, possibly modifying the activity of the different Ras isoforms and two of their main signaling pathways, PI3K-Akt and Raf-Erk; in a similar way, Sos1-Ras might regulate cell migration, in this case mainly through the PI3K-Akt intracellular pathway. According to our hypothesis, it has been described that mir-155-containing macrophage exosomes inhibit cardiac fibroblasts proliferation by downregulating Sos1 . The rol–/–H-ras/–/–N-ras . Moreover, the inhibition of Akt phosphorylation reduces the expression of collagen I in Sos1 KO fibroblasts, both in basal conditions and after TGF-β1 treatment. The increase in Akt phosphorylation observed in the absence of Sos1 is consistent with previous studies showing a significant activation of the PI3K pathway in fibroblasts expressing dominant negative mSos1 constructs 53 . This roroblasts . SimilarH-ras–/– , N-Ras–/N-Ras–/–  and K-raroblasts  exhibiteThe role of Sos1 in the regulation of the synthesis of collagen I, proliferation and cell migration that we describe in this study complements the previously cited studies of our research group that show similar effects in the absence of the H-, N- and K-Ras isoforms. All these data together suggest that the Sos1-Ras-PI3K-Akt and Sos1-Ras-Raf-Erk axes regulate the processes described above to a different extent. These data indicate that the absence of any of these mediators, Sos1 or any of the Ras isoforms, deregulates ECM production, proliferation and fibroblast migration. The presence of the three Ras isoforms seems to be necessary for the regulation of these processes, since the individual absence of each of them exerts quite similar effects, although in the case of H-Ras the regulation of these cellular processes seems to be more dependent on the Sos1-Ras-Raf-Erk pathway . These sThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.IF-C performed the experimental work and the statistical analysis. CM-S designed the study, conceived the experiments, analyzed the results, coordinated the study, and wrote the manuscript. Both authors reviewed the manuscript.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.


Oxaliplatin is a key drug for colorectal cancer that causes OXP-induced peripheral neuropathy, a dose-limiting effect characterized by cold and tactile hyperesthesia. The relationship between the sensory nervous system and modulation of the renin-angiotensin system has been described, focusing on pain and neurodegeneration in several animal models. We assessed the effect of the RAS modulator, ramipril, an angiotensin converting-enzyme inhibitor in a mouse model of OXP-induced acute pain syndrome. OXP was administered in Swiss mice at a cumulative dose of 15 mg/kg . RAM was administered i.p. every day from 24 h before the first OXP injection until the end of the experiments. We evaluated OIAS development and treatment effects by sensorimotor tests, intraepidermal nerve fiber and dorsal root ganglia-neuron immunohistochemical analyses, and sciatic nerve ultrastructural analysis. OXP-treated mice showed tactile allodynia and cold hypersensitivity, without motor impairment and evidence of nerve degeneration. RAM prevented cold sensitivity and improved recovery of normal tactile sensitivity in OXP-treated mice. Our finding that RAM alleviates OXP-induced pain is a step towards evaluating its therapeutic potential in patients receiving OXP treatment. Oxaliplatin, a platinum-based agent mostly used to treat colorectal cancer, frequently induces peripheral neuropathy, decreasing patients’ quality of life . DevelopThis study was conducted in accordance with the guidelines for the ethical care of experimental animals of the European Community (2010/63/EU) and was submitted to the French Ministry of Higher Education and Research and approved (number 11280#2017091510483336). Animal experiments are reported in compliance with ARRIVE guidelines . All effSwiss male mice  from Janvier labs (France) were housed in plastic cages and maintained on a 12 h light/dark cycle with food and water available ad libitum . Mice were randomly assigned to OXP or its vehicle (control group: CTRL), thereafter mice were randomly assigned to ramipril treatment or its vehicle, defining the following four treatment groups: control-vehicle (CTRL-VEH), oxaliplatin-vehicle (OXP-VEH), control-ramipril (CTRL-RAM) and oxaliplatin-ramipril (OXP-RAM).Behavioral tests were performed on day 1 (D1), D3, and D5 following the last OXP injection, except for the jump test, which was performed on D1 and D3 only to avoid acclimation of the mice to the test. All animals were submitted to the functional tests, except for the jump test, the day before the start of experiment (reference day: RD) to obtain the baseline for each animal. Immunohistochemistry and morphological analyses were performed one day after the last OXP injection, the time corresponding to the maximum of sensory impairment .per os in human, corresponding to a high dose (CIPN was induced by three injections of OXP (1 injection every 3 days) . Control mice received an equivalent volume of the OXP diluent . Treatments with ramipril  were started one day before the first OXP injection and administered each day for the next 10 days . The dosigh dose . Althougigh dose . RamipriMotor coordination was assessed using the rotarod test  . Three dMuscle function was assessed with a grip strength meter  . Mice weThermal nociception was assessed using the hot-plate test  . Mice weTactile allodynia was assessed using von Frey filaments . Mice were placed in a plastic cage with a wire mesh floor which allowed access to the paws. The plastic cage was covered with an opacity cup to avoid visual stimulation. The area tested was the mid-plantar left hind paw. Mechanical threshold was tested using a modification of the simplified up-down method . A test Thermal nociception was assessed using the jump to escape test . Noxiousn = 6) were euthanized by transcardiac perfusion with phosphate buffer saline (PBS), followed by buffered 4% paraformaldehyde (PFA) solution. Then, footpads were removed by punch biopsy (diameter of 3 mm), post-fixed overnight in 4% PFA, cryoprotected (30% sucrose) and frozen at −20°C. Sections were cut on a cryostat set to 20 mm and incubated overnight at room temperature with primary antibody to protein gene product 9.5 . Sections were then incubated with appropriate secondary antibody AF594-conjugated  during 2 h at room temperature. Epidermal nerve fibers were blindly counted under 400× magnification , according to established guidelines for Human  DRG per mouse were collected to assess the DRG neuron density by counting neuron cellular bodies visualized by PGP9.5, calcitonin gene-related peptide (CGRP), or substance P (SP) immunohistochemistry . Each DRn = 3/group) were captured at 3000x magnification and the number of myelinated fibers per mm2 counted to calculate the density.To assess the presence and morphology of unmyelinated nerve fibers, sciatic nerves were dissected after transcardiac perfusion with 2.5% glutaraldehyde diluted in Sorensen buffer, dehydrated, and embedded in Epon 812 resin . Semi-thin sections were stained with toluidine blue. Ultrathin sections were stained with uranyl acetate and lead citrate and observed under an electron microscope . Four images per animal . When statistical significance was identified by mixed-effects model statistical methods, individual comparisons were subsequently tested by Tukey’s multiple comparison test. Degree of significance was represented as follow: *The motor coordination of CTRL-VEH and OXP-VEH mice was evaluated using the rotarod test. There was no difference in the holding-time between OXP-VEH and CTRL-VEH mice on D1, D3, or D5 . Musculap = 0.5111, p = 0.9787, + and CGRP+ DRG neurons to explore the effect of OXP on neuropeptide depletion. There was no visible effect of OXP on peptidergic neuron population under our conditions  and the terminal level (IENF). There was no significant difference in the densities of IENF . Mechanical threshold returned to baseline 7 days after the last OXP injection   .vs. CTRL-VEH, p < 0.0001) and D3 , as the AUC (representing the number of jumps according to temperature) of CTRL-OXP-VEH mice was higher than that of CTRL-VEH mice , D3 , or D5   and significantly restored normal tactile sensitivity at D5   . Ramipri 0.6610) .vs. CTRL-VEH, p = 0.5265) and D3   and restored normal cold sensitivity on D3  (Ramipril did not influence cold responses in the control group on D1 (CTRL-RAM  0.9908) . Ramipri 0.9953) .Our main findings were: 1) the development and characterization of a mouse OIAS model, without evidence of nerve degeneration, and 2) ramipril alleviates cold allodynia and improves mechanical sensitivity in OXP-treated mice.Mice treated with short course of OXP did not develop abnormal motor coordination or muscular strength behaviors, suggesting that proprioceptive and motor Aα-fibers were not altered by OXP injections. This was confirmed by electron microscopy. The absence of motor deficit is consistent with observations made in other mouse strains; C57BL6J and BALB/c adult male mice . The hotThe relevance of our OIAS model resides in its being transient. Indeed, mice developed transient cold hypersensitivity, which consistently reproduced OXP-induced acute symptoms in humans, typically appearing with the second or third cycle of treatment during the infusion or within one or two days of OXP administration and disappearing within a few days . Clinica+ channels in the DRG, following a single dose of OXP (Molecular basis of OIAS was not fully elucidated, although several interesting tracks have been explored in the literature. Among them, studies suggested that OXP induces a hyperexcitability of primary sensory neurons related to a dysregulation or variation of the expression of ion channels involved in mechanical and cold sensitivity . More pae of OXP . Anothere of OXP .In summary, under our experimental conditions, three injections of OXP at 5 mg/kg in Swiss mice induced OIAS, characterized by transient tactile allodynia and cold hypersensitivity, associated with no impairment in motor performance or heat nociception, without overt nerve degeneration.via Mas receptor (+ channels (Ramipril, an ACE inhibitor, decreases Ang II synthesis and so AT1R/AT2R stimulation . Here wereceptor . Moreovechannels . Ramiprichannels . In theschannels . B1 recechannels . Thus, di.e. a non-painful stimulus, they are involved in the development of tactile allodynia, a non-nociceptive sensation. C and Aδ fibers are responsible for noxious and non-noxious perception of cold. Thus, the simplest explanation for such a difference in the effect of ramipril between tactile and cold allodynia is that these manifestations are linked to different fiber/DRG neuron subpopulations.In our study, beneficial effect of ramipril seemed to be unequal between cold and tactile pain. Tactile and cold sensitivity are transmitted by various types of fibers. As Aβ fibers participate in touch To conclude, we have shown that high dose of ramipril improves the recovery of cold sensitivity and alleviates OXP-induced tactile allodynia. Further investigation using a dose-response protocol to demonstrate whether a decrease in the dose of ramipril correlates with a decrease or not in the anti-allodynic effect in this model would be needed. As ramipril is a relatively inexpensive drug which has been demonstrated to be safe and well tolerated, our results favor clinical evaluation of the preventive therapeutic potential of ramipril in OXP-treated patients. Retrospective observational studies showed that ACEI administration in cancer patients under anticancer therapy is well tolerated . Moreove