There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer’s disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources . We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages. This issue has caused a great challenge on identifying the true outliers present in the complex data, and then remove them in the data process and analysis. In literature, this process of outlier removal has been typically overlooked. Accordingly, the data analysis might have led to inaccurate results or missing the true signals. In this study, we focused on development novel analytical strategy for outlierdetection from DNA methylation data, and then applied to the real datasets for two representative neurodegenerative diseases.The past 2 decades have witnessed exponential growth of genetic and epigenetic data generation, which substantially helps the advancement of biological and biomedical research. However, analysis of these datasets often suffers from the exclusion of outliers, leading to a decrease of power for detecting the true genetic or epigenetic markershttps://www.britannica.com/science/epigenetics3.Epigenetics refers to the study of genetic changes  that do not involve the alternation at the DNA sequence level , but they lead to the changes at the expression level or phenotype . The keyword “epigenetics” was first introduced in the early 1940s as a traditional  term by British embryologist Conrad Waddington to demonstrate the interactions between the genes and their products that promote the development and give rise to phenotype  of any organism. Since then, knowledge acquired from the epigenetics studies has transmuted in the domain of the genetics. So far, researchers have revealed various chemical alternations to DNA and then proteins denoted as histones which are connected to DNA very tightly in the nucleus through chromatin. These alternations can be detected while a specific gene is expressed either in the cell or the organism 6, microRNA7 and other types of RNA  regulation8, and protein expression modifications such as acetylation, sumoylation, ubiquitination, and phosphorylation10. DNA methylation regulation is a well-known epigenetic process. In mammalian genomes, 5-methylcytosine (5mC) methylation occurs when a methyl group is added to a cytosine, typically at the CpG dinucleotide12. These methylation and demethylation processes in cellular system are based on genetic factors, environmental factors, and their interactions14. Abnormal patterns of DNA methylation can lead to the creation and progression of various critical diseases19. The 5mC plays a key role in silencing X-chromosome and regulating gene expression at the specific locus or genome level21. Several DNA methyltransferase (denoted as DNMT) enzymes such as DNMT3A, DNMT3L, DNMT3B and DNMT1 catalyze the methylation23. The GC content and frequency of CpGs in a gene impact on the pattern of the methylation. For example, the CpG islands (denoted as CGIs), which are enriched CpG sites, are often hypo-methylated. The non-CGI sequences in the genome, which are the scattered CpGs in the genomic regions, are typically hyper-methylated24. Typically, approximately 80% of the CpGs in the genome are methylated, and de-methylation is a main mechanism to activate gene expression28. Hence, methylation leads to gene silencing32.Epigenetics includes various factors such as histone modifications, DNA methylation35. 5-Hydroxymethylcytosine (5hmC) is one of the most challenging topics in the field of epigenetics in the past 3–4 years. 5hmC has great potential for deep understanding of epigenetics in the brain tissue and its development. The oxidative product of 5mC is called 5hmC. The 5hmC mark was first reported in the T-Even bacteriophage about 7 decades ago36. So far, 5hmC changes have been found to be associated with several cancer and neurodevelopmental diseases such as Huntington’s disease and Alzheimer’s disease, suggesting it is a useful type of biomarkers for disease study37. Recently, 5hmC was detected in the brain of vertebrates as well as in other tissues40. In mice, 5hmC is reported to be abundant in the embryonic stem cells41. The abundance of 5hmC decreases upon differentiation43, but it increases again in the terminally differentiated cells (such as Purkinje neurons)38. The 5hmC is found in zygotes of rabbits, mice and bovines, and it is accumulated particularly in the paternal pro-nucleus along with a decrease of 5mC45. The various translocation (TET) protein-family members interfere with the level of 5hmC. An efficient chemical approach is currently being developed for measuring as well as labeling the 5hmC that represents the first map of distribution of 5hmC in a mouse brain, and its enrichment in the genes with a higher transcription46. The association of 5hmC with the specific gene-bodies at the time of differentiation and the maturation of neurons states that the 5hmC is spatially as well as temporally distributed in the brain tissue during the development of the brain. Transformation of 5mC into 5hmC is highly liable for the passive methylation. The 5hmC can be categorized into three sub-types according to their functions: 5hmC-A, 5hmC-B, and 5hmC-C. Among them, 5hmC-A primarily restrains the maintenance of DNA methylation during DNA replication43, 5hmC-B, which is prevented by the DNA repair protein, is used to active DNA demethylation49, 5hmC-C can obstruct the inclusion of the histone deacetylases, causing the development of the transcriptionally competent chromatin50.Another highly stable methyl cytosine variant next to 5mC is 5-hydroxymethylcytosine (5hmC)51 and The Encyclopedia of DNA Elements (ENCODE) project53. These regulators play critical roles in the cause and the progression of diseases such as neurodegenerative diseases, including Alzheimer’s disease (AD) and Down syndrome (DS). The roles of 5mC and 5hmC in these diseases have also been documented55. There are some lines of evidence supporting the importance of 5mC and 5hmC in the pathogenesis of AD59 and DS60. However, in the previous studies, only 5mC labeled samples, not 5hmC labeled samples, were considered in data preparation. Interestingly, many biological data suffer from the outlier features, reducing the power to detect the true markers1. There is a need to remove those noisy features in the beginning of the data process and analysis. In this study, we applied a well-known density based noise removal clustering algorithm, DBSCAN (“density based clustering of applications with noise”), to remove noise from a 5mC and 5hmC methylation profile (GEO ID: GSE74486) in the tissue of frontal cortex (FC) neurons for AD and DS. This procedure is followed by differential methylation analysis using Limma method and gene module identification using hierarchical clustering. We here performed two comparative analyses for differential methylation: (1) AD vs the matched control samples considering its FC neuron samples, and (2) DS vs the matched control samples using its FC neuron samples. We then identified the gene signature for each comparative study. The module with the highest average correlation score was considered as a potential methylated signature for AD as well as DS. Furthermore, different cluster validity index measures, such as Dunn Index (DI), Scaled Connectivity (SC), Silhouette Width (SW), Cluster Coefficient (CC), Maximum Adjacency Ratio (MAR), Centralization (Ctz) and Heterogeneity (Hg), were estimated to determine the quality and efficiency of the clustering. To verify the resultant gene signature, we applied Random Forest (RF) classifier to generate the group classification performance of the underlying samples of the signature. In addition, KEGG pathway and Gene Ontology (GO) term analyses were carried out to assess the biological significance of the resultant signatures. The results from our DBSCAN analytical approach provide some important insights into the understanding of epigenetic regulation in AD and DS.So far, epigenetic marks such as DNA methylation and histone modifications have been extensively examined in the cellular system, including some large-scale epigenomic datasets such as The Roadmap ProjectIn this study, we developed a new framework to identify the outlier-free DNA methylation signature for complex diseases. We then performed an extensive analysis for the classification of the methylation data for two neurodegenerative diseases, AD and DS, using the case and corresponding control samples Fig. . The fol61 . For the methylation analysis of AD, three AD FC neuron samples were used as the diseased samples, whereas eleven FC neuron samples were considered as the control samples. In case of the analysis of DS, nine DS FC neuron samples were used as the diseased samples, while the same eleven FC neuron samples were used as the control samples. In the GEO dataset GSE74486, the AD sample IDs were GSM1921521–GSM1921523 and the DS sample IDs were GSM1921524–GSM1921532, whereas normal sample IDs were GSM1921533–GSM1921543. The total number of Reference IDs  was 485,577.We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neuronsFirst, we eliminated the feature IDs starting with “rs” or “ch”, and kept only the CpGs. We then discarded the CpGs that had zero values in all samples or that had the missing value in any of the samples. Min-max normalization technique was used to normalize the data for each individual CpG. On the other hand, we collected the mapping information of CpG sites and official gene symbols (“UCSC_RefGene_Name”) through the annotation file “Illumina HumanMethylation450 BeadChip” (HumanMethylation450_15017482) (NCBI Ref. ID: GPL13534-11288). In the annotation file, there is either a one-to-many, many-to-one, or many-to-many relationship between CpGs and genes. We first chose the CpGs connected to each matched gene symbol, performing an average operation on all the CpG sites of each individual gene symbol to obtain a unique methylation data vector for each gene. Then, we conducted analyses for the two neurodegenerative diseases, AD and DS.62 using those unique gene vectors and filtered out the noisy features for each analysis. In detail, for each disease, we preliminarily estimated the knee point through KNN distance plot, and that knee value was used as the eps-neighborhood value. Other parameters were set by default . This generated a few density-based clusters, while each contained core, border and outlier (noisy) features. We then discarded these noisy features and further proceeded with noise-free features for the statistical analysis. The resultant cluster plot obtained by the DBSCAN was provided to visualize those core, border and outlier features clearly.Since the sample size is small, applying the noise removal clustering algorithm prior to using any statistical test is extremely helpful. Specifically, we conducted DBSCAN clustering algorithmeps) and minimum points (MinPts). DBSCAN is somewhat sensitive to parameter settings of eps and MinPts, but there is no specific theory that can completely guide the setting of its parameters64. The eps, the radius of the neighborhood around any point, was considered as MinPts is the minimum number of neighbors inside the eps radius. If a point has a neighbor count value higher than or equal to MinPts, the point is stated as a core point. Whenever the number of the neighbors of a point is less than MinPts, but the point belongs to the knee-point through KNN (K-nearest neighbor) distance plot for each disease. To evaluate the knee-point, first KNN distances were computed and then sorted. Thereafter, they were scaled in between 0 and 1 . The derivative was then estimated. Finally, the first point in which the derivative was higher than a certain value, 1, was considered as  knee-point. That corresponding scaled distance value of the  knee-point was considered as eps-neighborhood value.In DBSCAN clustering, two required parameters were epsilon  g is evaluated. Whenever the p value of the gene is less than 0.05, the gene can be defined as differentially methylated gene.After detection of the noise-free initial clusters through DBSCAN, we conducted Voom normalization73 were applied consecutively to generate the gene modules highlighted by different colors. After obtaining the gene modules, we estimated the scores of several cluster validity index parameters such as Ball_hall, Davies_bouldin, Dunn, G_plus, Gdi11, Gdi12, Gdi31 and Ray_turi.After obtaining the differentially methylated genes, we estimated the power for determining the soft-thresholding, and then applied this power to compute the adjacency matrix using Pearson’s correlation co-efficient. Next, we evaluated the topological overlap matrix (TOM) similarity and corresponding distance matrix from the adjacency matrix. Average linkage clustering and dynamic tree cut methodsAfter obtaining the gene modules, Pearson’s correlation coefficient (PCC) was computed among each gene-pair belonging to each gene module. Finally, the average correlation score for each cluster was obtained. The cluster that had the highest average PCC, was selected as the potential gene signature consisting of all differentially methylated genes.To verify the classification performance of the resultant signature, we applied Random Forest (RF) classifier using k-fold cross-validation (CV) , Cellular Component (CC) and Molecular Function (MF). A KEGG pathway or GO term whose enrichment p value was less than 0.05, was considered as statistically significant. For more detail about the flowchart of the framework, see Supplementary Figure In addition, we conducted gene set enrichment analysis using KEGG pathways and Gene Ontology (GO) terms available at DAVID online database (version 6.8)We found a total of 485,577 features (IDs) in the initial analysis of the data (GEO GSE74486). After removing the redundant IDs that started with “rs” or “ch”, the number of CpGs was reduced to 482,421. We then eliminated the CpGs that had zero values in all samples or had missing value in any of the samples. After this filtering, we obtained a total of 435,662 CpGs. Next, we performed min-max normalization technique to scale all the data for each CpG. We collected the mapping information of CpG sites and official gene symbols through the annotation file see “”. We firAfter applying the DBSCAN clustering algorithm on the 20,247 gene vectors, we filtered out the noisy features for the further analysis. In this regard, for the comparison of AD vs control samples, we first determined the knee point through KNN distance plot  were 83, 31, 28, 39, 21, and 27, respectively. Similarly, for the DS vs control analysis, using the power value , whereas the remaining 2 genes were hypo-methylated genes (MIR572 and IFNK). The heatmap for the corresponding cluster is represented in Fig. First, PCC was calculated between the pairwise genes in each cluster. The average PCC values of these six clusters for AD vs control, were 0.054, 0.446, 0.228, 0.154, 0.524 and 0.122, respectively. The fifth (red colored) cluster, which had the highest average PCC  on all samples using all of its participating features to classify two groups . The entire process was repeated 10 times. For the experiment: AD vs control, we computed accuracy and other evaluation metrics with three types of cross validations (CVs): 2-fold, 4-fold and 5-fold CV. The average accuracy values for these three types of CVs were 92.10%, 92.90%, and 92.90%, respectively, while the AUCs for these three types of CVs were 0.795, 0.783, and 0.771, respectively Fig. , S7. ForCYSLTR2 and LCE1F involved with a GO-CC term, GO:0016021 Integral component of membrane , whose association with AD was reported in Smith et al.75. Thus, CYSLTR2 and LCE1F are indirectly connected with AD through that GO term. On the other hand, CALML6, OR8G1 and OR52H1 were associated with a KEGG pathway, hsa04740: Olfactory transduction  and GO:0018149 Peptide cross-linking ], another two were GO cellular component terms [GO:0016021 Integral component of membrane  and GO:0001533 Cornified envelope ], and one GO molecular function term [GO:0005198 Structural molecule activity ]. Table ADH1B was involved in two KEGG pathways, hsa05204: Chemical carcinogenesis  and hsa00982: Drug metabolism-cytochrome P450 . HAMP, PGLYRP1, TINAG and TINAGL1 were associated in a GO-BP term, GO:0006955 Immune response . This disease is from the same GEO accession ID (GSE74486) but with different tissue (cerebellum). To validate our resultant gene signature obtained by previous dataset, first we selected all the features belonging to the gene signature , and evaluated the average accuracy and AUC. Specifically, there were initially 482,421 features (IDs) in the beginning of the analysis along with 13 DS cerebellum and ten control samples. We initially selected 443,020 CpGs, and then obtained 20,252 gene-vectors from them. Next, in DBSCAN, four resultant clusters were generated of which one cluster consisted of 16,736 core and 753 border features, while the number of core features for the remaining three clusters were 5, 10 and 5, respectively, Notably, we found no border feature for these three remaining clusters. The number of outlier features were 2743. We then selected only the non-outlier features belonging to these four clusters (https://gbiomed.kuleuven.be/apps/lcb/i-cisTarget/)79 on the resultant 89-gene signature for DS vs control (FC Neuron). Specifically, we used the 89 genes as the input to the i-cisTarget tool. We found 16 enriched features containing the normalized enrichment score (NES) RARRES1, as part of the 89-gene signature. Next four region IDs were chr12-reg6728, chr12-reg6726, chr12-reg6725 and chr12-reg6724 whose associated gene was LTBR that was part of the 89-gene signature. Notably, the list of 16 enriched features obtained from the generalized cis-regulatory enrichment analysis for the 89-gene signature by i-cisTarget web tool was mentioned in Supplementary File We performed the generalized cis-regulatory enrichment analysis (i-cisTarget) using its web tool  data with larger sample size [NCBI GEO ID: GSE134379]. This data consisted of 4,11,157 CpG cites (features) and a total of 404 samples including 225 AD samples and 179 control samples from cerebellum (CBL) brain region of Illumina 450K methylation array. We applied our proposed method on this dataset. After the removal of redundant CpGs and the mapping between CpGs and corresponding genes, we obtained a total of 20,190 gene vectors (non-redundant features). After applying the DBSCAN clustering algorithm on the 20,190 gene vectors, we discarded the noisy features. In this regard, we first estimated the knee point through KNN distance plot  in the tissue of FC neuron for two neurodegenerative diseases: AD and DS. We first performed various pre-filtering analyses to initially remove the redundant CpGs. In this study, two sets of analyses had been conducted, (1) AD vs control, and (2) DS vs control, both using the methylation data from the FC neuron tissue, which is a critical tissue to study AD and DS. In our study, we preliminarily estimated the knee-point through KNN distance plot for each diseaseThere are a couple of reasons of obtaining big difference in the results: (1) the imbalanced dataset  and (2) noise found in the datasets. The total number of initial features was 4,85,577, while total number of samples used for AD vs control experiment was 14 and total number of samples used for DS vs control experiment was 20. Thus, due to these extreme imbalanced datasets, the intermediate result up to the signature detection might be varied. For example, use of statistical test with smaller population size of samples are difficult. As a result, classification accuracy was be varied highly. The second reason is high noise in the dataset. To reduce noisy features, we applied DBSCAN clustering technique initially to detect noisy features for improve the later results. For the additional dataset (AD vs control in CBL brain region), we also obtained good average accuracy  in our analysis. Finally, our resultant outlier-free signatures might be useful for the potential detection of onset or progression of neurodegenerative diseases, as methylation is critical in such diseases.Supplementary Information 1Supplementary Information 2Supplementary Information 3Supplementary Information 4


Background: The emergence of COVID-19 as a pandemic has resulted in the need for urgent development of vaccines and drugs and the conduction of clinical trials to fight the outbreak. Because of the time constraints associated with the development of vaccines and effective drugs, drug repurposing and other alternative treatment methods have been used to treat patients that have been infected by the SARS-CoV-2 virus and have acquired COVID-19.Objective: The objective of this systematic scoping review is to provide an overview of the molecular mechanism of action of repurposed drugs or alternative treatment medicines used to attenuate COVID-19 disease.Method: The research articles or gray literature, including theses, government reports, and official news online, were identified from four databases and one search engine. The full content of a total of 160 articles that fulfilled our inclusion criteria was analyzed and information about six drugs  and four Traditional Chinese Medicines  was extracted.Results: All of the repurposed drugs and complementary medicine that have been used for the treatment of COVID-19 depend on the ability of the drug to inhibit the proliferation of the SARS-CoV-2 virus by binding to enzyme active sites, viral chain termination, or triggering of the molecular pathway, whereas Traditional Chinese Medicine plays a pivotal role in triggering the inflammation pathway, such as the neuraminidase blocker, to fight the SARS-CoV-2 virus. In December 2019, a novel type of viral pneumonia was discovered in Wuhan, Hubei Province, China. The disease has been officially named “COVID-19 (CoronaVirus Disease 2019)” and the virus has been named SARS-CoV-2 by The International Committee of Taxonomy of Viruses . The newThe causation of COVID-19, SARS-CoV-2, is from the family of CoVs, single-stranded RNA viruses that look like a crown under a microscope (coronam is the Latin word for crown) and contain spike glycoproteins on the envelope. It is categorized as a betaCoV, which has an elliptic or round and often pleomorphic form, with a diameter of 60–140 nm. Acute respiratory illness appears to be the most common manifestation of COVID-19 infection. The stages of symptomatic infection range from mild to severe; most infections are not severe :Mild symptoms are present at the time of inoculation and the incubation period, such as malaise, a dry cough, and fever. During this time, the SARS-CoV-2 virus multiplies and primarily establishes residence in the respiratory system. The virus binds to its target through the angiotensin-converting enzyme 2 (ACE2) receptor in human cells .Localized inflammation in the lungs and viral multiplication occur in the second stage of the disease. Patients develop a viral pneumonia, with symptoms including fever, cough, and hypoxia. A chest roentgenogram or computerized tomography usually shows bilateral infiltrates or ground glass opacities. Increased lymphopenia and neutrophil-lymphocyte ratio (NLR) are evident in blood tests .Approximately one out of every six patients transition into the third stage of the illness, which is the most severe and is manifested as an extrapulmonary systemic hyperinflammation syndrome. Systemic inflammation is present during this stage, as well as a decrease in suppressor, helper, and regulatory T cell counts . Shock, The latest trend shows that human-to-human spread is the main mode of transmission, which occurs through respiratory droplets resulting from sneezing and coughing. Erosol transmission could also occur in closed areas. Infection might also happen if someone touches a contaminated surface and then touches their own eyes, nose, or mouth . The mosAt this moment, the therapeutic approaches to handle COVID-19 are only supportive. There is neither a vaccine to prevent infections nor clinically approved antiviral drugs to treat COVID-19. Therefore, the identification of drug treatment options is critical for responding to the pandemic. Clinical trials for vaccines are currently underway in many countries. However, the efficacy of the vaccines, how long immunity will last, or if infection can occur even if a person possesses a high level of antibodies will not be clear for at least 1 year after injection . FurtherAccording to a previous study, disruption of the liver has been reported in patients diagnosed with SARS  and MERSDrug repurposing has become a model for the rapid development of drugs against infectious diseases, especially in the current global emergency, as it allows the saving of time as well as being a more cost-effective form of drug development. Drug repurposing involves the uncovering of existing therapeutic agents for the treatment of new illnesses or the identification of new therapeutic targets for existing drugs , includiSeveral previous studies have reviewed drug repurposing for the treatment of COVID-19. However, most reviews have only focused on the mechanism of action of a single drug. In this review, we included 10 medications, comprising six drugs and four complementary medicines. These medications were selected based on their initial successful treatments of COVID-19 patients at the beginning of the COVID-19 outbreak, before it was declared as a pandemic. A few months after the pandemic was declared, the six drugs in this study were officially announced by WHO to be included in a multicountry trial known as SOLIDARITY; the four complementary medicines were included in the national guideline for management of COVID-19 by China, the first country infected by COVID-19. In this comprehensive systematic review of these 10 medications, we discuss the mode of action from a molecular mechanism perspective to attenuate COVID-19 in the human system. By understanding different molecular mechanisms of 10 important drugs instead of a single drug molecular mechanism, researchers could gain a deeper insight of the pivotal genes or mechanisms that should be targeted for future study to ameliorate this pandemic condition, which can also lead to the development of new and effective drugs for the treatment of COVID-19.https://www.ncbi.nlm.nih.gov/pubmed/), Google Scholar (https://scholar.google.com/), Science Direct (https://www.sciencedirect.com/), and Semantic Scholar (https://www.semanticscholar.org/). Gray literature search such as guidelines, conference papers, theses and dissertations, government reports, research and committee reports, and abstracts were obtained from WHO, Chinese Clinical Trial Registry (http://www.chictr.org.cn/abouten.aspx), U.S National Library of Medicine, China Center for Disease Control and Prevention, China Dissertation Database, China Important Conference Papers Database, and online official news websites. Most of the gray literatures were from China because China is the first country to encounter this disease and provides more data to tackle this disease. Different combinations of the following keywords: NCov-2019, COVID-19, 2019-nCoV, SARS-CoV-2, 2019 novel coronavirus, Wuhan virus, drug repurposing, medication, treatment, traditional Chinese medicine, and alternative and complementary medicine, were used in the literature search through a three-level search strategy based on standardized descriptors defined by the Medical Subject Headings algorithm. A secondary search was based on screening of the reference list of all the relevant studies identified in the direct search. The entire potentially relevant studies were evaluated after title screening to exclude irrelevant information. Only studies that reported the repurposing of drugs and TCMs for COVID-19 and contained information about the structure of the chemical constituents, in vivo or in vitro studies, case reports, treatment of patients diagnosed with COVID-19, and molecular mechanisms, were extracted and assessed.A systematic scoping review was conducted from January 1, 2020, until March 18, 2020, by including papers that were published and not published before March 18, 2020, on reported drug repurposing and Traditional Chinese Medicine (TCM) as treatment options for COVID-19 used in patients from different countries to reduce publication bias, increase the comprehensiveness and timeliness of the review, and foster a balanced picture of available evidence . The revShuang Huang Lian Kou Fu Ye, TCM combination of Bu Huan Jin Zheng Qi San and Da Yuan Yin, Xue Bi Jing Injection, and Qing Fei Pai Du Tang). The 133 articles in this review consist of five guidelines, 16 clinical trial registries, 11 from official news, 42 in vitro and in vivo studies, 40 reports presenting in vitro and in vivo outcomes, and 19 clinical findings and treatment efficacy. A flowchart of the progressive study selection and numbers at each stage is shown in The primary search identified 8,074 published and unpublished papers, of which 2,132 were from PubMed, 2,775 from ScienceDirect, 720 from Google Scholar, 1,190 from Semantic Scholar, and 1,257 from Google search engine. A total of 3,798 duplicates were excluded, leaving 4,276 articles for screening by title analysis. 841 eligible published and unpublished papers were identified after excluding 3,435 papers that had a different theme from this systematic scoping review. In conclusion, the content of 841 papers was fully analyzed, of which 681 were excluded according to our exclusion criteria, which was medication that was not administrated as treatment for COVID-19 before March 18, 2020. To establish any differences within a drug class, the drug classes were divided into different subclasses and individual drugs. Repurposed drugs used for treatment of COVID-19 consisted of six drugs  and four TCMs  have been repurposed for the treatment of COVID-19 and discuss the initial mechanism of action of these drugs from a molecular perspective for the treatment of coronaviruses or other viruses. A summary of the mode of action of all the drugs in this study is shown in in vivo (in vitro at a lower amount (17 nM)  ; however (17 nM) , thus reBetacoronavirus) (These drugs were the first repurposed drugs that were used to treat patients diagnosed with COVID-19 . The admnavirus) . Therefonavirus) . Lopinavnavirus) . Successnavirus)  and Spainavirus) .pro enzyme by binding to the enzyme’s active site, with neither of them having a higher binding affinity than the other  is a 1′-cyano-substituted adenosine nucleotide analogue prodrug  that wasin vitro test utilizing primary human lung epithelial cell cultures, remdesivir was potently antiviral against coronaviruses that consisted of Bat-CoVs, zoonotic Bat-CoVs, SARS-CoV, MERS-CoV, and circulating contemporary human-CoVs  and in vitro using Calu-3 cells with MERS-nanoluciferase compared with other antiviral drugs such as lopinavir, ritonavir, and interferon beta  is another nucleotide analogue developed by Toyama Chemicals for treatment of influenza virus infections  that hasTo study the effect of favipiravir on COVID-19, favipiravir was approved for the treatment of COVID-19 disease on February 15, 2020. in China. A pilot study of a nonrandomized control trial at The Third People’s Hospital of Shenzhen reported significantly better treatment effects in terms of disease progression and viral clearance compared with lopinavir/ritonavir treatment . A randoIn contrast to remdesivir and favipiravir, chloroquine (CQ) is an antimalarial drug considered as one of the drug candidates that exhibit good inhibitory effects on SARS-CoV-2 at the cellular level . Chloroqin vitro studies. Chloroquine successfully inhibited viral replication of HCoV-229E, SARS-CoV, MERS-CoV, and EBOV in various cell lines  Vahl, according to Pharmacopoeia of People’s Republic of China   . These cBased on the Guidelines of Diagnosis and Treatment for COVID-19 Version 5 by the National Health Commission (NHC) of the People’s Republic of China on February 8, 2020, three TCMs have been used depending on the severity of the condition of COVID-19 and symptom differentiation .Atractylodes lancea (Thunb.) DC., 6 g of Citrus × aurantium L., 10 g of Magnolia officinalis Rehder and E. H. Wilson, 9 g of Agastache rugosa (Fisch. and C. A. Mey.) Kuntze, 6 g of Lanxangia tsao-ko (Crevost and Lemarié) M.F.Newman and Skornick., 9 g of Ephedra sinica S., 10 g of Hansenia weberbaueriana (Fedde ex H. Wolff) Pimenov and Kljuykov, 9 g of Zingiber officinale Roscoe, and 10 g of Areca catechu L.  by Wu Youke, respectively, from Dynasty Ming (Atractylodes lancea (Thunb.) DC., which attenuated the influenza A virus within 5 days through the TLR7 signaling pathway by upregulating the Toll-like receptor 7 (TLR7), MyD88, tumor necrosis factor receptor-associated factor 6, and IFN-β mRNA expression in the lung tissue of mice infected with influenza A virus   J. Presl, 9 g of Alisma plantago-aquatica L., 9 g of Polyporus umbellatus (Pers.) Fries, 9 g of Atractylodes macrocephala Koidz., 15 g of Poria cocos (Schw.) Wolf , 16 g of Bupleurum chinense DC., 6 g of Scutellaria baicalensis Georgi, 9 g of Pinellia ternata (Thunb.) Makino, 9 g of Zingiber officinale Roscoe, 9 g of Aster tataricus L. f., 9 g of Tussilago farfara L., 9 g of Iris domestica (L.) Goldblatt and Mabb., 6 g of Asarum sieboldii M., 12 g of Dioscorea oppositifolia L., 6 g of Citrus trifoliata L., 6 g of Citrus × aurantium L., and 9 g of Agastache rugosa (Fisch. and C. A. Mey.) Kuntze. This TCM is a combination of four combinations of well-known prescribed classic TCMs from Treatise on Cold Damage Diseases, which are Ma Xing Shi Gan Tang, She Gan Ma Huang tang, Xiao Cai Hu Tang, and Wu Ling San   and evenyndrome) . Similaryndrome) . Qing FeLing San .Ma Xing Shi Gan Tang (MXSGT) is known for antipyretic effects and is commonly used to treat pneumonia, influenza, and other respiratory diseases (p < 0.00001) (p < 0.05) on day 7 of consumption of the decoction besides being effective and safe for the treatment of community-acquired pneumonia (diseases . A syste0.00001)  and showneumonia . This efneumonia . Furtherneumonia . This deneumonia .She Gan Ma Huang tang (SGMHT), inhibits mast cells from releasing substances during inflammation, regulates the viscera’s function, and promotes the apoptosis of eosinophils (Aster tataricus L. f. and Iris domestica (L.) Goldblatt and Mabb.) from a modified version of SGMHT (Xiao Cai Hu Tang and Wu Ling San) function differently and did not exhibit function against acute airway obstruction. Xiao Cai Hu Tang (XCHT) is known as a TCM for liver treatment, particularly chronic hepatitis B. This TCM modulates STAT3 expression and indirectly suppresses the hepatitis B virus according to western blot analyses and real time PCR results (Wu Ling San (WLS) has been used to treat impairments of the regulation of body fluid homeostasis in Japan, China, and Korea  used in this concoction possesses hepatoxicity in a clinical trial from an extensive literature review study through standardized causality assessment algorithms, Roussel Uclaf Causality Assessment Method  Diels (patients . Unlike patients . Xue Bi .) Diels .Xue Bi Jing Injection relieved or reduced severe pneumonia by triggering the inflammation pathway through downregulation of TNF-α, IL-6, and IL-8 on the 3rd, 7th, and 14th day after treatment, although it did not significantly influence the release of leptin (Xue Bi Jing Injection reduces the severity of pneumonia in COVID-19 patients and lowers the side effects on the organ functions.It was initially developed for activating blood circulation to remove blood stasis, cooling the blood, and clearing toxic heat . Howeverf leptin . This suf leptin . This meThese results indicate that TCMs can be used as complementary medicine for the treatment of patients during this pandemic disease; however, the mechanism of action of these TCMs still requires further investigation and validation.The treatment of COVID-19 with the six repurposed drugs discussed in this study is dependent on the ability of the drug to inhibit the proliferation by binding to the enzyme active sites, viral chain termination, and triggering of molecular pathways. In contrast to the six drugs, the four TCMs discussed in this review were initially used to treat influenza and SARS by acting as neuraminidase blockers and trigger the inflammation pathway. In this review, we provide a framework for better understanding of the mechanism of action of repurposed drugs and TCMs and their involvement in the molecular pathway for inhibiting viral replication such as SARS or MERS.Instead of focusing on one drug mechanism of action, we analyzed repurposed drugs that are currently involved in worldwide clinical trials to elucidate their molecular mechanism. The inclusion of gray literature provides more data to better understand the properties and effects of repurposed drugs. However, there are limitations to our findings because the quality of the evidence and risk of bias were not evaluated due to the nature of a scoping review. Furthermore, there is still a lack of concrete evidence for the mechanisms of action of the drugs and their curative effect on COVID-19 because the clinical trial is still ongoing. Further experimental validation is needed to provide more concrete evidence.Understanding the different molecular mechanisms of these drugs instead of just one drug molecular mechanism can provide insights on the pivotal genes or mechanisms that should be targeted in future studies and lead to the development of effective drugs for the treatment of COVID-19 in the future.


R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of  As an important component of the earth ecosystem, soil moisture content (SMC) is directly involved in the exchange of water and energy among surface water, groundwater, and atmospheric vapor ,2. MeanwSMC distribution is influenced by several interacting factors, such as soil characteristics, vegetation coverage, and climatic conditions. Therefore, it is relatively difficult to obtain large-scale SMC information efficiently with the traditional single-point measurement methods such as the drying method and digital probes ,8. CompaSynthetic aperture radar (SAR), as an active microwave remote sensing technique, has the capacity to penetrate surface vegetation, which is quite suitable for the quantitative inversion of farmland surface SMC. Consequently, there are many scientific methods and models for SMC retrieval with SAR data, such as the multi-temporal , empiricFor the former one, the vegetation backscattering could be effectively separated to obtain the backscattering information of the bare soil through the vegetation microwave scattering model, in which a water cloud model (WCM) is a commonly used model . For exaα polarimetric decomposition  model has been successfully employed in previous SMC retrieval ). Furthermore, this study defines the equivalent number to analyze the relative importance of each feature parameter more objectively (Formula (18)).EN means equivalent number, The SMC prediction results are not only influenced by the inversion model but also by the different combination of characteristic parameters, so it is necessary to further analyze the effect of each characteristic parameter on the SMC inversion results. Each possible combination is composed of 3/cm3, and the R2 was greater than 0.5. After screening, 160 groups in SVR and 146 groups in GRNN were used to calculate the equivalent number. The counting results are shown in The equivalent number of each input feature was calculated by a partial combination of SVR and GRNN models, where the RMSE of the test set of these combinations was required to be less than 0.035 cmAlthough the equivalent number of each input feature in the two models are different, the appearance trend is the same, from large to small arranged as i et al.  that σVVtude DEM 9.96/306 α were all sensitive to SMC. Judging from the equivalent number of the three parameters, the counting results of H (141.68/306) and A (137.22/306) are relatively close, and α (148.81/306) led with a slight advantage. The statistical results led to the conclusion that the addition of polarimetric decomposition features is of superiority in SMC retrieval. This is supported by the fact that Özerdem et al. found that H, A, and α are sensitive with SMC [On the one hand, with SMC .α features, the CNNR models were able to estimate SMC with RMSE between 0.0208 and 0.0245 cm3/cm3 and R2 between 0.78 and 0.89. To further investigate the quantitative impact of H, A, and α, the comparison result of SVR, GRNN, and CNNR under six selected combinations is listed in R2 and RMSE values of SVR, GRNN, and CNNR with polarization decomposition feature combination were obviously better. Through further calculating, we could conclude that the R2 of SVR, GRNN, and CNNR increased by 0.1028, 0.1524, and 0.1524, respectively, and the RMSE of SVR, GRNN, and CNNR decreased by 0.0027, 0.0012, and 0.0019 cm3/cm3, respectively. Undoubtedly, the polarization decomposition features are beneficial to raising the accuracy of SMC retrieval.On the other hand, after mixing H, A, and During this paper, different input features in all possible combinations were analyzed to confirm and consider the main features applicable to farmland areas. It is difficult to effectively estimate vegetation in the process of inversing farmland surface SMC using remote sensing data. Different from other literatures considering the effect of vegetation cover on SMC ,26, one Meanwhile, this paper defined an equivalent number to evaluate the relative importance of each characteristic parameter, which can better reflect the differences between groups, so it is more objective and reasonable than simply counting. The results verify the important effects of the radar backscatter coefficient, altitude, local incidence angle, and vegetation index on the retrieval of farmland surface SMC by counting its equivalent number.Another contribution of this paper was that regression convolutional neural networks were constructed to SMC retrieval with the fusion of SAR and optical data. This proposed CNNR model could fully consider the spatial distribution of features and extract advanced features reflecting the spatial and temporal differences of SMC. Meanwhile, the pooling layer commonly used in the traditional convolutional neural network structure was removed, which not only ensured the integrity of the extracted feature information but also accelerated the network training and predicting speed. In addition, the method employed does not require evaluating the impact of vegetation, thus avoiding errors in the process of estimating vegetation.Furthermore, in the same dataset, this CNNR model could better fuse satellite data of different observation modes and types, which made the utilization of satellite images more efficient. Test results confirmed that CNNR has higher applicability to retrieve SMC in our experimental area than the traditional Oh model, SVR, and the GRNN model.The results proved that merging the intensity information and phase information of SAR data can effectively raise the accuracy of SMC. Under similar scenarios and data sources, the overall accuracy of the proposed approach shows good performance in comparison to other methods in the literature, and some main methods for retrieving SMC are listed in α decomposition model was employed to investigate and verify the influences of SAR phase information to SMC. Actually, there have been several polarimetric decomposition techniques used in inverting SMC, so further research could be carried out together with other common polarization decomposition methods to improve the retrieval accuracy. Secondly, vegetation indices vary with plant growth stage and vegetation type, and the original band that involved better versatility and consistency than vegetation indices could be applied in future work. Thirdly, although Sentinel-1 SAR imaging is not affected by environmental factors such as clouds and fog, the red and near-infrared bands used in this model are sensitive to weather. Thus, the application of this method is occasionally restricted by the availability of Sentinel-2 optical imagery.Firstly, since the Sentinel-1 data used in this paper were limited to dual-polarized data, only the H/A/α decomposition method was firstly implemented to extract polarization decomposition features that are merged with other feature parameters to form the input vector. Then various retrieval methods were employed to estimate surface SMC.This paper aimed at evaluating the capability of using Sentinel multi-source data to retrieve SMC over farmland areas by regression convolutional neural networks. In our proposed approach, the H/A/R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. These results indicated that CNNR has great potential for estimating soil moisture from Sentinel multi-source data and could provide good prospects for local farmland irrigation and water conservation.The experimental results confirmed that vegetation index and polarization decomposition features could characterize vegetation contribution effectively over farmland areas. The addition of polarimetric decomposition features is effective to improve the accuracy of soil moisture retrieval results. Among these retrieval models, CNNR performed the best compared to the traditional Oh model, SVR, and GRNN, whose


Patients with obstructive sleep apnea (OSA) might be at risk of gout because of pathophysiological mechanisms that can lead to hyperuricemia and eventually gout or because of shared risk factors between both diseases. The objective of the present study was to investigate the risk of gout in patients with OSA.A population-based case-control study using the UK Clinical Practice Research Datalink GOLD including all patients aged 40 years and older with a first diagnosis of gout between 1987 and 2014. Gout cases were matched by year of birth, sex, and practice to non-gout controls. Conditional logistic regression estimated the risk of gout with an earlier diagnosis of OSA. Analyses were adjusted for lifestyle factors, comorbidities, and recent drug use.One hundred eleven thousand five hundred nine cases were matched with 210,241 controls. Patients with OSA were at increased risk of gout . However, this association disappeared  after adjustment for smoking status, body mass index (BMI), alcohol use, a history of heart failure, diabetes mellitus, renal function, and recent use of diuretics and other medications. Among females with OSA and patients with OSA associated with heart failure, renal impairment, or higher BMI, the risk of gout was however still increased when compared to the total control population.This study showed that the observed association between OSA and gout disappeared after adjustment. Gout is the most common inflammatory arthritis , affectiAlternatively, the relationship could also be explained by shared risk factors of gout and OSA, such as age, obesity, metabolic syndrome, renal impairment, and heart failure .Two prospective studies in large United Kingdom (UK) primary care databases have demonstrated a 1.5-fold increased risk of developing gout among patients with OSA , 9 with The objective of the present study was to investigate the risk of gout in patients with OSA, while accounting for all relevant potential confounders, including CKD and heart failure.http://www.cprd.com). CPRD contains the computerized medical records of approximately 13 million patients under the care of general practitioners (GPs) in the UK, representing 6.9% of the total UK population [Data for the present study were obtained from the Clinical Practice Research Datalink (CPRD) in the UK, previously known as the General Practice Research Database −0.203 × ). In addition, we identified diagnostic codes for stages of CKD. When there were multiple records on the same day, the best eGFR was chosen. The following categories were used to stratify for renal function by eGFR: CKD 1 (eGFR > 90 ml/min), CKD 2 (eGFR 60–89 ml/min), CKD 3 (eGFR 30–59 ml/min), CKD 4 (eGFR 15–29 ml/min), and CKD 5 (< 15 ml/min).The following variables were considered as potential confounders and were assessed prior to the index date: smoking status, BMI, alcohol use, socioeconomic status, a history of hypertension, diabetes mellitus (as recorded by either a diagnostic code for diabetes mellitus or a history of prescription(s) for anti-diabetic treatment, British National Formulary Chapters 6.1.1 and 6.1.2), hypercholesterolemia, postmenopausal status/hysterectomy, acute myocardial infarction, stroke, or heart failure. The use of the following medication was assessed in the 6 months before the index date: thiazide diuretics, loop diuretics, beta-blockers, calcium channel blockers, angiotensin-converting enzyme inhibitors (ACE-inhibitors), angiotensin II receptor blockers (ARBs), low-dose aspirin, statins, non-insulin antidiabetic drugs (NIADDs), insulin, or benzodiazepines. In addition, the most recent eGFR before the index date was assessed. Electronic lab test data were used to extract the eGFR. Furthermore, when only serum creatinine measurements were available, these were used to estimate the eGFR by the use of the abbreviated MDRD formula (186 × (serum creatinine/88.4)Conditional logistic regression was used to estimate the risk of gout associated with a diagnosis of OSA . In the analyses, risk was expressed as odds ratios (OR) with corresponding 95% confidence intervals (CIs). Potential confounders were included in the final model if they independently changed the beta-coefficient for OSA by at least 5% or when a consensus about inclusion existed within the team of researchers, supported by clinical evidence from the literature. Missing data of confounders such as BMI, smoking status, alcohol use, and renal function were treated as separate levels using dummy variables. OSA exposure was further stratified by gender, age categories, and the presence of important confounders. Finally, we studied the effect of univariately adding the most important confounders to the main analyses as well as adjusting simultaneously for these confounders.2 SD 5.3 in cases vs. 26.8 kg/m2 SD 4.8 in controls). On average, gout cases used alcohol more often (73.9% in cases vs. 65.5% in controls) and were more likely to be ex-smokers than controls (34.2% cases vs. 26% controls). With regard to comorbidities, gout cases more often had a history of hypertension, heart failure, or reduced renal function. They were also more frequently recent users of diuretics.Table Patients with OSA had an almost doubled risk of gout . Also, in comparison to patients without OSA, those with OSA and a history of heart failure had an almost doubled risk of gout . Furthermore, recent use of loop diuretics  and use of thiazide diuretics  was also associated with an increased risk of gout. The risk of gout among patients with OSA also further rose with increasing renal impairment , adj. OR 3.93; 95% CI 1.06–14.56 for CKD 4 (eGFR 15–29 ml/min) . Patients with OSA and hypercholesterolemia also had a 6% decreased risk, although not statistically significant .Our study showed that the almost doubled risk of gout with OSA disappeared after adequate statistical adjustment for BMI, renal function, heart failure, and recent use of diuretics. Notwithstanding, subgroups of patients with OSA, more specifically women and those with a history of heart failure, who had recently used diuretics, who had an eGFR between 59 and 15 ml/min, or who had a BMI above 30 kg/m2, still had a 2- to 4-fold increased risk of gout.The absence of an overall association between OSA and gout in our study contrasts with a 1.5-fold increased risk of gout with OSA that was reported by two previous studies , 9 whichHeart failure is probably under-recorded in large observational studies based on diagnostic codes . SpecifiUnexpectedly, we found that the risk of gout with OSA disappeared in men after adjustment for confounders, while in women, the risk remained elevated. It is widely accepted that females are at lower risk of gout, as a result of the uricosuric effect of estrogens in women before menopause . HoweverObesity, which itself is associated with hypoxemia, is the main risk factor for the development of OSA. In more obese patients, OSA is aggravated with more severe oxygen desaturations and hypoxemia, which may explain why the risk of gout remains high in the highest BMI category , 34 Fig.Our study had several limitations. First, there probably is underreporting of both OSA and gout, especially in the less severe cases , 35. MisOur study had several strengths. First, we were able to include a large number of patients with gout and controls. The findings of this study are therefore likely to be generalizable to patients with gout and OSA in the total UK population . Second,This study showed that the observed association between OSA and gout disappeared after extensively adjusting for BMI, heart failure, diuretics, and renal function, in particular. As the latest guidelines for the treatment of gout by the British Society of Rheumatology recommend to discuss the use of ULT with every patient, even after a first attack of gout, we think that it is important that physicians are aware that gout occurs more frequently in the presence of various comorbidities, among which OSA. Our study also emphasizes the importance of using frequently recorded electronic lab test data to assess renal function in UK primary care data, rather than READ codes.


After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints. Trends in the global burden of the disease from 1990 to 2016 show that OA is the second most rapidly rising condition associated with disability, with a 46 percent increase in years lived with disability, just behind diabetes at 52 percent2. With the aging population and higher rates of obesity, this burden is expected to rise. The rapid increase in the prevalence of OA will lead to a growing impact and major challenges for health care and public health systems. OA can occur in different joints in the musculoskeletal system, such as the knee, hips, back, hand, and temporomandibular joint (TMJ), having a multifactorial etiology that includes: excessive mechanical stress, hormonal changes, genetics, aging and others5. The TMJ is a unique model to study early bone changes in OA, as the articular bone surface is covered only by a thin layer of fibrocartilage in the TMJ condyle. Osteoarthritis of the TMJ (TMJ OA) is a multi-system disease, involving numerous pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain8.Osteoarthritis (OA) affects millions of people worldwide, causing them many years with pain and disabilityin vivo OA disease models now benefit from high-resolution cone-beam tomography imaging (HR-CBCT)9. HR-CBCT scans allow diagnosis of the bone environment with sub-millimeter resolution comparable to micro-CT, but with a much lower radiation dose10, and have been widely used by clinicians and researchers14. As treatments to reverse the chronic damage of TMJ OA are unavailable, early diagnosis may provide the best opportunity to prevent extensive and permanent joint damage. However, current diagnosis is based on pre-existent clinical/imaging signs and symptoms markers using standard protocols recommended for Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), meaning to diagnose TMJ OA degradation of the joint must have already occurred16. The DC/TMD criteria are based on pre-existent condylar damage, such as subcortical cysts, surface erosions, osteophytes, or generalized sclerosis that are present mainly in the later stages of the disease. Towards an early diagnosis that is predictive of disease status, animal studies indicate that the bone microarchitecture18 is an important factor in the OA pathogenesis initiation, preceding articular cartilage changes19, and should be investigated in human studies. There is an estimated increase in OA prevalence over the next decades, which reflects in more data acquisition, demanding advances in computational machine learning and data management22. For this reason, there is a need for precise data mining algorithms, data capture, standardization, management and processing from multiple centers to provide personalized treatment and diagnosis25. For disease diagnosis, machine learning approaches have been applied in the medical field29. Most of the studies have pointed out algorithms and multi-source biomarkers to predict the disease status, such as XGBoost30, LightGBM31, deep learning algorithms32, random forest algorithms27, etc. The models have been tested with different features, including radiographic and magnetic resonance (MRI) data34, proteomics28, and clinical information27 for creating patient-specific prediction models23. However, most studies addressed the OA involvement in the knee. For the temporomandibular joint, we found two studies that were done by our group evaluating only the morphological changes in mandibular condyles36. In addition, most of the literature is focused on multi-center database, or late stages of OA (chronic stages) assessed using routine exams. Here, we addressed surrogate biomarkers such as the radiomics, which can be useful to explore the subchondral bone organization and maybe play a pivotal role in a true early diagnosis of the TMJ OA.Studies using 37. Data management includes standardized imaging38, clinical15 and biomolecular39 acquisition, and control of patient information from multiple data sources, with standardized demographic for matching OA patients and healthy controls. We have evaluated fifty-two variables to determine the most relevant integrative feature pools using machine-learning algorithms to detect TMJ OA status 10. We extracted 20 imaging features  with duplicate samples for each patient, controlling the standard curve and limiting expression as can be seen in Supplementary Fig. We selected proteins that have previously been detected in the TMJ synovial fluid of OA patientsDiagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: Recommendations of the International RDC/TMD Consortium Network and Orofacial Pain Special Interest Group”15. Our results show that only RangeAssMax and RangeUnaMax were not statistically significantly different between the TMJ OA and control groups. The clinical features that presented statistically significant differences were correlated with pain or limited by pain. For example, for the maximum opening without pain (RangeWOpain), patients were instructed to open their mouths until they start to feel pain within their TMJs. This approach reduces the amount of opening for the TMJ OA patients that often present pain during opening; however, when the patients were asked to open the mouth as much as they could even with pain  the values were not statistically significant between the groups. In Fig. We present the Mann-Whitney U test in Supplementary Fig. We had 55 features shown in Tables 30/LightGBM31 model to make diagnostic predictions. More details can be found in the Method section. We demonstrate  cross-validation.45. To obtain patient-specific, precise diagnostic information, Data Science has become indispensable in medicine, with the integration of data capture, management/processing and in-depth analysis with rigorous and standardized protocols47.We report here, the diagnostic performance of machine learning approaches to predict TMJ osteoarthritis status. Through data acquisition, management and processing, we achieve one of the main challenges of healthcare delivery, which is to integrate the patient data information from multiple sources for accurate diagnosis and meaningful indicators of individual health48. Our result in Supplementary Fig. 38, where a decreased energy was associated with bone sclerosis/loss, and the increased values for HarCor and Entropy was correlated to bone sclerosis/loss. For the knee OA diagnosis, Brahim et al.26, (2019) using imaging features, evaluated the performance of machine learning algorithms to detect the disease, with x-rays radiographic from the public dataset Osteoarthritis Initiative (OAI). Their results showed an OA detection with 82.98% of accuracy using Random Forest and Naive Bayes classifiers. Even though the results were good, a standardization of the images was necessary to classify the images from multi-centers correctly. Here, we addressed this challenge, using a rigorous protocol for the imaging markers; however, our sample size (n = 46 per group) in comparison with Brahim et al.26 (n = 516 per group) was not enough to get a good accuracy only using the radiomics, where we found an AUC of 0.70 approximately . For this reason, we also included clinical features and biomolecular information that improved our diagnostic model performance. Also, we have found in the literature only two papers showing the application of machine learning approaches in the temporomandibular joint osteoarthritis diagnosis, and both were focused on developing a model only for bone surface classification36.In the carefully controlled data acquisition methods of our study, all subjects had the same imaging acquisition protocol, all clinical assessments were performed by the same pain specialist, and a single investigator collected the clinical, biological and imaging data. This study database was composed initially of 107 subjects, and 15 subjects were excluded due to incomplete or inadequate quality of data. As part of the data management and processing, we extracted radiomics information from each subject’s HR-CBCT scan Fig.  to obtai15 criteria. We have chosen features measured in both groups, as described in Fig. 49, and now our study shows its improved diagnostic performance to predict OA status in conjunction with other features and machine learning approaches. Interestingly, the interaction of headaches with TGF-β1_Sal together was one of the top features with >80% mean contribution to information gain in our statistical learning models. A possible clinical explanation is that patients with headaches had increased levels of this protein, as previous studies have indicated the expression and correlation of this cytokine with mandibular bone degradation in TMJ OA patients53. Other clinical markers included in our disease model were: RangeWOpain and its interaction with PAI-1_Sal. As TMJ OA patients present with pain in their TMJs, the decreased amount of mouth opening without pain was an important disease sign with an AUC of 0.70; and its interaction with PAI-1_Sal was an exciting finding, as this feature was increased in the OA patients , 39 were females and only 7 males. The role of MusSor in this interaction may be due to differences in pain sensitivity in women and men56, and to the central sensitization caused by painful osteoarthritis as patients with temporomandibular disorders also presented higher prevalence of muscle-related symptoms57. In our study, the variables and/or interactions of: PAI-1_Sal*Headaches (AUC: 0.697), RangeWOpain (AUC: 0.7), Headaches (AUC: 0.778), TGF-β1_Sal*Headaches (AUC: 0.707), Gender*MusSor (AUC: 0.716), VE-cad_Sal*Headaches (AUC: 0.698), Headaches*ShortRLowGLE (AUC: 0.692)), PAI-1_Sal*RangeWOpain (AUC: 0.701) were the clinical features presenting >80% mean contribution to information gain, included in LightGBM and/or XGBoost prediction models. A study from 201358 that also investigated clinical markers , and genes  with support vector machines and probabilistic neural networks found a high classification performance, showing an AUC of approximately 0.95. This study indicates that our findings may be improved with the addition of genetics information. However, besides the challenges involving genetic studies, here, we used relatively simple approaches to assess our features, that could be applied to the clinical practice.All clinical data were obtained using the DC/TMD39 to be expressed in the TMJ synovial fluid and plasma and to be correlated with the condylar morphology in OA patients. It should be highlighted that in the present study, those markers were obtained from saliva and blood samples utilizing less invasive procedures for the patient, and circulating levels of pro-inflammatory proteins have been shown to contribute to the pathophysiology of disorders of the TMJ59. In addition, saliva has been described as a promising, accurate and non-invasive tool for a reliable diagnosis44. A study from Kellesarian et al.60, showed the cytokines profile in the synovial fluid of patients with TMJ disorders, and the most proteins that we used in our study were listed in their systematic review. For the knees OA, Hear et al.28, (2014) also evaluated the performance of machine learning algorithms using artificial neural network (TreeBagger decision tree) and cytokines from serum as features to classify patients with TMJ OA, rheumatoid arthritis and normal. Interestingly, the authors found only 12 statistically significant differences in the cytokines from the 38 studied, and the patients were classified based on late symptoms. The CNN showed the high performance to classify the patients (sensitivity and specify higher than 90%), and the author suggests that a combination of the cytokines is more critical for the classification than the individual levels, going towards to the findings of our present study.For the biomolecular markers, no differences between OA and control subjects . As results, the highest F1 score was for eOA diagnosis (0.78) and the lowest for non-RA (0.36) with GLMNET algorithm. In our study, we had a similar sample size and goal (detection of OA in an early stage), and we found a slightly better F1 score, close to 0.83. A recent study, in 201727, evaluated machine learning approaches for the identification of new biomarkers for knee osteoarthritis diagnosis. The baseline number of variables for each participant consisted of 186 features, including questionnaires , radiography, magnetic resonance scores, physical/clinical examinations, biomarkers from serum, and urine, etc. The algorithm used was RGIFE , and for testing the model, they selected 10-fold cross-validation. The authors found five good prediction models, including different subsets of features in each. The overall discrimination of knee OA among the patients was considered good with good (AUC between 0.80 and 0.90). Each prediction model presented multi-source biomarkers such as clinical information, imaging-based information, pain, food questionnaires, and molecular markers. These results suggest that the disease has a complexity etiology, corroborating with our findings and confirming that there is a need to investigate the association of clinical, imaging, and proteins to better categorized this complex disease.In a diagnostic perspective, Ahmed 64. For osteoarthritis, studies using mathematical algorithms for diagnosis and personalized treatment decisions are increasing65. We have previously shown the diagnostic performance to predict the disease status based on the condylar surface morphology and deep learning approaches40, and now we show an integrative approach based on clinical, imaging radiomics and biomolecular patient-specific data. A limitation of this study is that the cross-sectional study design does not allow assessment of the disease progression and how different disease stages affect the proposed biomarkers. Other studies that assessed early stages of the knee OA using machine learning approaches33, used subjective radiologic interpretation of 2D x-rays, rather than the high-resolution 3D images included in the present study. Here, with the use of the radiomics, clinical, and protein information, our predictive model with XGBoost + LightGBM and 1378 features/interactions showed an accuracy of 0.823, AUC 0.870, and F1-score 0.823 to determine disease status. Future studies using the proposed machine learning models and longitudinal data will provide better information on the feature’s behavior and disease progression.Nowadays, the complex, high-dimensional, and biomedical data from multiple sources benefit from data science, computational advances, and machine learning approaches to improve knowledge in terms of diagnosis, disease classification, clustering data, and disease progression predictionIn conclusion, our in-depth statistical learning analysis was based on the integration and interactions of 52 features. We screened the diagnostic performance of each feature Figs.  and buil“Strengthening the Reporting of Observational studies in Epidemiology” (STROBE) guidelines for observational studies66. All experiments were performed in accordance with the guidelines and regulations approved by the Institutional Review Board approval (HUM00105204 and HUM00113199) from the University of Michigan and the informed consent was obtained from all participants.We followed the 15 for TMJ osteoarthritis diagnosis. The patients were diagnosed as early stages of TMJ osteoarthritis when they presented: pain in at least one TMJ for less than 10 years, TMJ noise during movement or function in the last 30 days and crepitus detected during mandibular excursive movements. The Control group subjects were recruited by advertisement and evaluated for the absence of TMJ OA clinical and radiographic signs and symptoms. The diagnosis for the TMJ OA group and side of choice (left or right) was confirmed utilizing the radiographic criteria16, including initial stages of subchondral cyst, erosion, generalized sclerosis and/or osteophytes. For the matching control condyle, the side of choice was the one without any clinical or radiographic findings. The exclusion criteria for the TMJ OA group were patients with middle to chronic TMJ OA diagnosis, evaluated when they present more than 10 years of TMJ pain diagnosis and/or severe stages of bone destruction, subchondral cyst, erosion and generalized sclerosis evaluated using the hr-CBCT by a radiologist.After the Institutional Review Board approval (HUM00105204 and HUM00113199) from the University of Michigan, we enrolled patients and subjects from January 2016 to December 2018 that composed our TMJ OA and Control groups, respectively. This cross-sectional study sample was composed of 92 patients, 46 TMJ OA and 46 age and sex-matched control subjects who were selected based on rigorous inclusion criteria. The general health conditions of the participants included: age between 21–70 years old, no history of cancer, no history of jaw joint trauma, no previous surgery in the TMJ or recent jaw joint injections, absence of systemic diseases; no current pregnancy and no congenital bone or cartilage disease. All patients were examined by a single temporomandibular disorders specialist at the Hospital of the University of Michigan  through the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)Our study was composed by 3 main sub-groups of variables, which were: biomolecular features , imaging features (composed by trabecular bone radiomics and morphometry) and clinical features.60, was due to our previous studies that detected these markers in the TMJ synovial fluid and saliva of OA patients, showing correlations with bone surface changes39.We evaluated 14 proteins in serum and saliva associated with arthritis initiation and progression, such as nociception, inflammation, angiogenesis and bone resorption, which were: 6ckine, Angiogenin, BDNF, CXCL16, ENA-78, MMP-3, MMP-7, OPG, PAI-1, TGFb1, TIMP-1, TRANCE, VE-Cadherin and VEGF. However, the expression of 6ckine was not expressed in the serum and saliva samples in this study, and MMP-3 was not expressed in saliva. The raw data can be seen in the Supplementary Fig. 67.The participants had 5 ml of venous blood collected by a trained nurse at the University of Michigan. The blood was centrifuged for 20 minutes at 1000 RPM to separate only the serum that was then aliquoted in 2 ml Eppendorf tubes and stored at −80C. For the saliva collection, the participants received a 14 ml sterile test tube with a funnel inserted; they were instructed to tilt their head forward and drip the saliva off into the tube until 2 ml was collected. They were informed to not spit, talk, or swallow during this processet al.68 and Huang et al.69). Supplementary Figures Custom human quantibody protein microarrays obtained from RayBiotech, Inc. Norcross, GA, was used to quantitatively assess the saliva and serum samples for the 14 specific biomarkers. Each participant had duplicates run for the saliva and serum samples , Vertical Range Unassisted Maximum (mm), Vertical Range Assisted Maximum (mm).The same investigator collected and measured the clinical signs and symptoms of the participants based on the DC/TMD3. The images were exported in DICOM (.dcm) using the manufacture software: i-Dixel  and optimization manufacture filter: G_103 + H_009. Finally, the images were coded and de-identified to avoid investigator bias in the statistical analysis.We acquired cone-beam computed tomography scans of each subject using the 3D Accuitomo  machine at the University of Michigan, School of Dentistry. The protocol for the temporomandibular joint high-resolution CBCT was field of view 40 × 40 mm; 90 kVp, 5 mAs, scanning time of 30.8 s and a voxel size of 0.08 mm38. The region analyzed was the internal condylar lateral region . For the descriptive analysis, we showed the median in addition to the mean, the 95% confidence intervals and the standard deviation. The Mann-Whitney U test was used to test our hypothesis and we used a two-tailed test with α of 5%.70 from a two-sample Mann-Whitney U test to evaluate the significance of each feature  and two demographic variables (age and gender). First, we normalized all features to have zero mean and one standard deviation. Next, we calculated the AUROC (Area under the Receiver Operating Characteristic curve), p-value and q-valueWe applied the 10 times’ 5-fold CV by taking 4 folds as training and the remaining one-fold as validation with 10 times’ repetition. At each time, we normalized the original 52 features denoted as F1 based on the training subjects and then took the product between each pair of them to generate additional 1326 interactions and denoted the set of 1378 features as f ∈ F1|AUC of f > 0.7} and {f ∈ F2|AUC of f > 0.7} for feature pools met F1 and F2, respectively.We calculate the AUC for each single feature in 30, Light Gradient Boosting Machine , Extreme Gradient Boosting (XGBoost; method Supplementary information.Supplementary information2.Supplementary information3.Supplementary information4.Supplementary information5.


AV samples showed reduced absolute abundances of bacteria in general and specifically of lactobacilli by qPCR, but 16S rRNA gene sequencing and amplicon sequence variant analysis revealed that Lactobacillus remained the dominant taxon in 25% of the AV samples studied. The other AV samples showed high relative abundances of Streptococcus agalactiae and, unexpectedly, the anaerobes Gardnerella vaginalis and Prevotella bivia in more than half of the AV samples studied. Yet, despite increased relative abundance of these potential pathogens or pathobionts in the AV bacterial communities, the AV samples only slightly stimulated Toll-like receptor 4 and showed reduced activation of Toll-like receptor 2/6, receptors of two pathways central to mucosal immunity. Our findings indicate that the reduced total bacterial abundance with associated enrichment in certain pathobionts in AV might be mainly a consequence of the inflammatory conditions and/or altered hormonal regulation rather than bacteria being a major cause of the inflammation.While bacterial vaginosis (BV) is a well-known type of vaginal dysbiosis, aerobic vaginitis (AV) is an inflammatory condition that remains understudied and under-recognised. It predisposes women to serious complications including urogenital infections and pregnancy problems. Here, we investigated the bacterial community in AV to explore its possible role in AV pathogenesis. We collected vaginal lavage fluid samples of women ( Aerobic vaginitis (AV) is a highly relevant, yet still underexplored vaginal condition, first described by Donders et al. in 2002 . PatientChlamydia infection [Gardnerella vaginalis and other species [G. vaginalis, Atopobium vaginae, Prevotella, Mobiluncus, and Dialister species overgrow the niche  increased in the vaginal microbiome of AV patients [Selective bacterial culturing, microscopy data, and quantitative PCR (qPCR) have described features of the bacterial community in AV. Besides the abovementioned depletion of patients ,15,16, hAV shares some of the hallmarks of BV, such as a malodorous vaginal discharge and an elevated vaginal pH. Because of this and since BV is better known, AV is sometimes misdiagnosed as BV. Wet-mount microscopy can easily distinguish these conditions, but microscopy combined with adequate scoring systems remains largely underused in clinical practice . Such a 16S rRNA gene sequencing and processed the samples at a single nucleotide level, allowing us to discriminate closely related biological variants, termed amplicon sequence variants (ASVs) [Enterobacteriaceae) can trigger inflammation is through activation of TLR4, the bacterial-responsive TLR that induces most often and most vigorously a pro-inflammatory response [Prevotella species), and lipoteichoic acid. The latter is present in the cell wall of Gram-positive bacteria, including lactobacilli and group B Streptococcus [16S rRNA sequencing, qPCR, and TLR assays brought some specific features of AV to light and allowed us to discriminate AV, BV, and reference samples. Our findings therefore did not only increase our understanding of AV, but are also relevant for diagnostics.Here, we hypothesized that an integrated molecular approach could provide additional information regarding the important members of the bacterial community in AV and improve our understanding of AV pathogenesis. Following this, the primary goal of our study was to provide a detailed, community-wide DNA-based insight into the vaginal microbiome of AV patients. We used high-throughput s (ASVs) . Becauses (ASVs) , relevanresponse . The dimtococcus ,23,24,25Lactobacillus-dominated microbiota (n = 18) or suffering from BV (n = 20) or AV (n = 20) were recruited at the general hospital Heilig Hart, Tienen, Belgium. The study was reviewed and approved by the ethical committee of the Heilig Hart hospital in Tienen  and all patients gave their explicit consent before sampling.58 premenopausal women between 18 and 51 years of age showing a normal Candida. The group where the wet-mount microscopy showed a lactobacillary grade I or IIa, without signs of AV, BV, or Candida [Wet-mount microscopy was used for diagnosis, as previously described . The ima Candida , acted a Candida . The flu16S rRNA gene were performed as two technical repeats. The primer design and cycling conditions were based on the paper of Kozich et al. (2013) [For every DNA sample, two PCR reactions amplifying the V4 region of the . (2013)  (Table SLactobacillus-dominated (NL) groups was based on the ANCOM method [p-values by capping the false discovery rate at 10% with the method of Benjamini and Yekutieli [Sequencing data were analyzed using the DADA2 package  in the RM method . Read coekutieli . Quantif® Green chemistry . DNA was extracted from 500 µL and 100 µL of overnight cultures of Lactobacillus crispatus LMG12005 and Streptococcus agalactiae ATCC49447 and from 500 µL and 75 µL of a culture of THP-1 monocytes , using the same protocol as for the samples  and used to determine bacterial and human cell concentrations by qPCR, using the StepOnePlus real time qPCR system  and SYBR samples . The obt5 cells/well , was diluted to a final concentration of 10 ng/mL and used for the HEK-Blue™ hTLR4 cells. PAM2CSK4 was resuspended and diluted to a final concentration of 50 ng/mL and used as a positive control for the HEK-Blue™ hTLR2-TLR6 cells. The cells were incubated with the standards/samples for 24 h at 37 °C, 5% CO2. The embryonic alkaline phosphatase secreted by the cells was quantified by adding 50 μL of supernatant of each well (in duplicate) to 100 μL of substrate solution . After 20 min of incubation shielded from light, absorbance was measured at 405 nm.The ability of the samples to stimulate TLR4 and the dimer of TLR2 and TLR6 was estimated through the use of the HEK-Blue™ hTLR4 and the HEK-Blue™ hTLR2-TLR6 reporter cell line , which produces secreted embryonic alkaline phosphatase in response to TLR4 or TLR2-TLR6 stimulation. The day prior to an experiment, cells were seeded in a 96 well plate at a concentration of 2.5 × 10All statistics and visualizations were performed using RStudio , ggplot2, and ggpubr packages. For pairwise comparisons between the groups (qPCR and TLR reporter cell experiments), pairwise Wilcoxon tests were used, corrected for multiple testing with the Holm-Bonferroni method.https://github.com/swuyts/AV_BV_study and the data can be accessed through ENA accession number PRJEB29686. qPCR data is available as The complete sequencing analysis pipeline is available at n = 20) or BV , diagnosed by conventional phase contrast microscopy (n = 18) with a normal Lactobacillus-dominated (NL) microscopy image were characterized by 16S rRNA gene sequencing. Although the classification in NL, BV, and AV of our samples was initially based on wet-mount microscopy  [16S rRNA gene sequencing analysis confirmed that most samples of the NL and BV group showed distinct community compositions as expected (Lactobacillus-dominated group (NL) was almost exclusively dominated by Lactobacillus sequences (>95% relative abundance) (Lactobacillus species (31.9–58.0%) were observed. In four samples , no or only few lactobacilli could be detected (<5%), but this is in agreement with previous work that not all healthy women are dominated by lactobacilli [Atopobium, Gardnerella, Prevotella, Dialister, Sneathia, and Megasphaera as the most important genera.The vaginal bacterial communities of women suffering from AV   and in fobacilli . In the Lactobacillus , Prevotella , Streptococcus , and Gardnerella . The occurrence of Prevotella was associated with higher AV scores , Prevotella, and Streptococcus (more abundant in AV)  in seven samples but dominant (>50% relative abundance) in none. These differences did not reach statistical significance for AV versus NL, possibly due to a lack of statistical power (low sample size). However, when comparing AV versus BV, many genera were found to be significantly differentially abundant, with the strongest effects for Streptoccocus (more abundant in AV), Megasphaera, Atopobium, and Sneathia  is one of the three main features of microscopic AV diagnosis. Nonetheless, lactobacillary grades are just one element in the composite score of AV, explaining the high relative abundance of lactobacilli in some samples . Upon compositional analysis of differential abundance , we fout in AV) . Also, DAV group . DialistLactobacillus, Prevotella, Streptococcus, and Gardnerella constituted the most abundant genera across the entire dataset in terms of relative abundance, the most abundant ASVs of these genera were classified further by EZ BioCloud [Lactobacillus ASVs were found in the dataset. The four most abundant Lactobacillus ASVs were classified as L. iners, L. crispatus/acidophilus/gallinarum, L. gasseri/hominis/taiwanensis/johnsonii, and L. jensenii/fornicalis, in agreement with community groups III, I, II, and V proposed by Ravel et al. (2011), respectively. Interestingly, although L. iners was the most abundant ASV in BV and our reference group, followed by L. crispatus/acidophilus/gallinarum, this was not the case for the AV group, where three of the five subjects were still dominated by L. gasseri (or related). In total, 26 different Prevotella ASVs were found in the dataset. The most abundant Prevotella ASV was classified as Prevotella bivia, while two others were classified as Prevotella timonensis and a fourth abundant Prevotella ASV could not be classified further than to genus-level. The dataset contained six Streptococcus ASVs and two of them occurred in multiple samples at higher relative abundances (up to 100% and 27.9% relative abundance), which could be identified as Streptococcus agalactiae  and Streptococcus anginosus, respectively. The Streptococcus ASVs were limited to the AV group, except for two NL samples, NL12 and NL02 . Our dataset also contained four Gardnerella ASVs of which two occurred often (in 24/58 and 17/58 samples) and in higher relative abundances (up to 99.6% and 47.2%). Both sequences were found in BV, AV, and the reference group and were both derived from Gardnerella vaginalis strains. These ASVs matched perfectly with the sequences of G. vaginalis variants reported by Callahan et al., 2017 [G. vaginalis ASV1)  .Lactobacillus iners 1 was significantly underrepresented in the AV group  were overrepresented in the AV group as compared to the NL group. However, the latter differences were not statistically significant, indicating that the mere bacterial composition of the vaginal microbiota can be insufficient to discriminate AV from NL. On the other hand, similar to the genus-level analysis, multiple significant differences in abundance were found for AV and BV, such as for ASVs belonging to the genera mentioned before , followed by the BV group (median = 1.4 × 106 cells/mL) and then the reference group (median = 7.4 × 105 cells/mL) (9 CFU/mL) and was significantly higher than those of the reference group  and the AV group . Looking at specific genera with genus-specific primer sets, the reference group had the highest concentration of lactobacilli . Even though the relative abundances were lower, the BV group had somewhat similar concentrations as the reference group for both lactobacilli (median 8.2 × 106 CFU/mL) and streptococci (median = 3.102 CFU/mL). In contrast to the streptococci, the association of AV with two other bacterial groups, Enterobacteriaceae and Staphylococcus, was less evident from our data. For Enterobacteriaceae, 14 of 20 AV samples showed amplification , with three samples showing estimated concentrations above 104 CFU/mL (2 CFU/mL), but not the reference samples (amplification in 10/18 samples with a median of 5.4 × 102 CFU/mL). For staphylococci, we found that several samples also did not amplify Staphylococcus DNA (Ct > 38), . However, in the other samples, we observed relatively similar concentrations of staphylococci in the three groups . Nevertheless, despite their similar DNA abundance, their activity could be altered between the two states (AV and reference) because of the different abundance of lactobacilli. For example, we tested six vaginal Lactobacillus isolates of various species and their secreted molecules in spent supernatant were all able to inhibit the growth of S. aureus, in contrast to Streptococcus salivarius and E. faecium  a. Higherells/mL) b. The BV4 CFU/mL e. This w faecium . This cap = 1.7 × 10−5 and BV-NL p = 0.004), intermediate values for NL (median = 99), and the lowest values for AV .When comparing the total bacterial load as a ratio of the concentration of total bacteria to the concentration of human cells, the three groups could be clearly distinguished f. The hip = 0.04, pairwise Wilcoxon test) as compared to the reference group (p = 0.001) and the reference group (p = 0.023). In contrast, in the TLR2-TLR6 assay (Lactobacillus-dominated reference group showed the highest stimulation (median = 1.99), followed by the BV group  and finally the AV group . However, we acknowledge that while such minimalistic models are useful to eliminate other signals and focus just on particular immune pathways, this approach is too limited to generalize to a possible inflammatory response of the host to the resident bacteria since many other pathways could be involved as well.After characterization of bacterial abundance and taxonomic composition of the AV microbiome, we subsequently monitored the ability of the microbially characterized vaginal lavage samples to stimulate the innate immune system through TLRs. Limited activation of TLR4 was observed in most samples of the AV group, which yielded only slightly higher activation . Therefore, our results support the previous suggestions of an increased prevalence of streptococci in AV  ASVs with high relative abundance and our qPCR analysis did not indicate elevated concentrations. Nonetheless, we could have underestimated the role of staphylococci since it is plausible that the activity of the staphylococci is different in AV due to the lack of competing microbes such as lactobacilli with bacteriostatic effects on S. aureus growth. Although some AV samples did show Lactobacillus as a dominant taxon in the 16S rRNA analysis, overall, Lactobacillus  showed a lower abundance in the AV group as compared to the NL samples in the compositional analysis. Additionally, in accordance with previous studies, the AV samples tested here also exhibited lower absolute numbers of lactobacilli in qPCR analyses. This could suggest that although lactobacilli are diminished in numbers, this did not result from other bacteria becoming more abundant and outcompeting them, but rather from host (immune) factors killing or inhibiting their growth .Studies using fections  and is tPrevotella, Gardnerella, and Dialister from the AV samples. These genera were not previously found in AV and were only associated with BV (or community state type IV) up to now [Gardnerella, Prevotella, and Dialister [Prevotella bivia  is known to produce enzymes such as sialidases that might contribute to mucosal degradation and thus epithelial thinning in AV [P. bivia have been linked to preterm delivery [In contrast to previous culture-based studies and what the name ‘aerobic” vaginitis would suggest, we also unexpectedly recovered the anaerobic p to now ,14. Thisialister ,36. As tng in AV ,38. Furtng in AV  and bothdelivery . Therefodelivery ,40,41.Lactobacillus-dominated commensal community might be an important part of maintaining homeostasis in the reference group. Furthermore, the lower activation of TLR2-TLR6 observed in the AV samples as compared to reference samples, and possibly, therefore, lower anti-inflammatory activity, combined with the slight activation of pro-inflammatory TLR4 signalling might affect this delicate immunological balance in the vaginal mucosa. This lack of clarity highlights the importance to study the interactions between the immune system and the AV microbial community.For the assay evaluating TLR2-TLR6 responses, induction was most remarkable in the reference group. This could possibly be explained by a large presence of lactobacilli in these samples . ImportaStreptococcus and P. bivia [S. aureus might shift to a more virulent phenotype because of the absence of lactobacilli [The possibility of immune dysfunction itself as an underlying cause of AV has already been the subject of previous research . While wP. bivia ,42, whilobacilli . SeveralLactobacillus restoration since the inflammation and associated antimicrobial environment will complicate the survival and efficacy of the probiotics in these patients. Selection strategies will therefore benefit from in vitro prediction models taking this into account [Here, we describe a first explorative study of the AV microbiome using NGS, however currently our main shortcoming is our sample size. Therefore, our findings should be confirmed in larger and more diverse cohorts. If such studies confirm the hypothesis that the altered microbial community is the result of the disease rather than the cause of it, this would hold important implications regarding the treatment options and AV as a concept. For example, it would argue against the use of antibiotics for the treatment of AV  as they  account .