Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.Limited knowledge about a substantial portion of protein coding genes, known as “dark” proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at  Nevertheless, a recent survey of the human proteome identifies approximately one third of proteins as understudied or “dark”, with few or no published molecular annotations and not the subjects of substantial current research [Observational data from clinical genetics and systematic mutagenesis in mice suggest that almost all of the roughly 20,000 proteins encoded in the human genome are needed for normal human function   proteins according to the Reactome database  and Pharos web site (April 2023): 1,351 of total 5,679 dark proteins. To place those dark proteins that have not been annotated in Reactome into the context of Reactome pathways, we first collected a variety of pairwise relationship features from multiple data sources and then trained a random forest using functional interactions extracted from annotated Reactome complexes and reactions as positive data points and random pairs as negative data points. After that, we predicted whether or not a pair of proteins could functionally interact with each other based on the trained random forest model. Based on the predicted functional interactions (FIs), we inferred how likely a dark protein could potentially functionally interact with pathways annotated in Reactome . Though In total we have collected 106 gene/protein pairwise features, including 48 tissue specific gene co-expressions from GTEx , 31 cancBased on predicted functional interactions between a protein and a set of other proteins that are annotated for a specific pathway in Reactome, we developed three scores to measure the likelihood or strength of an interacting pathway for a protein . The firWe conducted the interaction pathway analysis for all proteins, including both dark proteins and non-dark proteins, and proteins that have or have not been annotated in Reactome. As expected, proteins annotated in Reactome have significantly higher values than proteins that have not been annotated in Reactome across all three scores  with TdaSingle cell omics technologies, especially single cell RNA-sequencing (scRNA-seq) technology, are generating unbiased extremely large datasets at the single cell level, allowing researchers to study molecular interactions and pathways with unprecedented details inside and between cells. To validate the predicted scores for interacting pathways, we conducted a gene expression correlation analysis using a blood scRNA-seq dataset generated by the Tabular Sapiens project . This daTo check if the scRNA-seq results are biased to annotated and not-dark proteins, we compared the distributions of Pearson correlations between annotated and not-annotated proteins and dark proteins and not-dark proteins  workflow based on the pre-trained BERT  language model  to embedSimilar to the scRNA-seq analysis results, the NLP-based analysis also shows a significantly positively skewed distribution of Pearson correlations between NLP-based pathway annotation scores and interaction scores based on the predicted FIs , presumamanual annotation of interacting pathways for 20 dark proteins.xlsx” file in Supplemental Results).To further explore the validity of the interacting pathway predictions, literature and database searches were performed on twenty randomly selected dark proteins to determine whether existing published experimental data supported roles for these proteins in their respective predicted interacting pathways. PubMed searches were performed using both the gene names and UniProt identifiers. GeneCards, UniProt, and GO entries were also searched for functional annotations with direct experimental evidence. The results of this analysis are shown in Direct experimental evidence suggesting a function for the dark protein in the interacting pathway was found for twelve of twenty proteins. For example, KLHL9 is predicted to interact with the Mitotic Metaphase and Anaphase pathways. Evidence supporting this interaction has been provided by experiments showing that the KLHL9 protein and another substrate-specific adaptor, KLHL13, form a complex with the Cullin 3-based E3 ligase, Cul3, which is essential for mitotic division and is required for correct chromosome alignment in metaphase, proper midzone and midbody formation, and completion of cytokinesis . AnotherThe function of four dark proteins, IGKV1D-17, IGKV1D-43, RBMY1E, and TRAV5, in this analysis could not be determined in literature searches. However, each had a close family member protein(s) with a known or suspected function in the predicted interacting pathway. Two dark proteins, AGAP3 and HSP90B2P, had functions that did not seem relevant to the interacting pathways and two, POM121B and STAG3L1, were predicted pseudogenes.In summary, our manual literature annotation supports the majority of predicted interacting pathways for 20 randomly selected dark proteins, further validating the feasibility of our workflow.To provide the community with a resource to learn biological functions and therapeutic potentials of understudied proteins and proteins that have not been annotated in Reactome, we have developed a web portal. The portal was developed on the foundation of the Reactome web application by implementing a new homepage and enhancing the pathway diagram widget and overlay features.https://idg.reactome.org, a progressive single-page web app powered by JavaScript widgets, where users may search for interacting pathways for a gene or a protein based on gene symbol or UniProt accession number, respectively. The homepage presents multiple views for users to explore the found interacting pathways. The scatter plot view . The coverage analysis using Reactome’s pathway enrichment analysis tool  indicates that 1,351 out of 5,679 Tdark proteins  have been annotated across 864 Reactome pathways. As expected, the majority  remain unannotated in Reactome. Using our novel functional interaction based pathway interaction approach and applying an FI score cutoff of 0.8, we found that 2,217 out of 4,328 (51% based on gene names) of these unannotated Tdark proteins have at least one interacting pathway in Reactome, with an enrichment score (FDR) of less than 0.05. Our web portal provides a range of features for users to try different FI score cutoffs, increasing the likelihood of identifying interacting pathways for even more dark proteins.Reactome has annotated about 50% of human protein coding genes, including some categorized as dark proteins based on the IDG project  , 37 or ohttps://www.gtexportal.org/home/datasets. The file we used was the gene read counts file, GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_reads.gct.gz. Samples with RIN values less than or equal to 6.0 and tissues with fewer than 30 samples were filtered out. Cancer-specific RNA-seq gene expression data was acquired in November, 2019 using a customized Python script that utilized the GDC API as described in this document, https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/. The script is available at https://github.com/reactome-idg/gather-app/blob/master/python/switch/tcga_gather.py. Outlier analysis based on PCA’s loading matrix (https://github.com/reactome-idg/gather-app/blob/master/python/scripts/R/functions.R) was conducted, and samples with z-scores of the first PC  greater than or equal to 3 were marked as outliers. The percentage of outliers in each tissue or cancer was less than 5%. The gene counts were normalized to cpm (count per million) values and then subjected to pairwise Spearman correlation analysis after removing outliers. A total of 50 and 32 correlation matrices were generated for GTEx and TCGA, respectively. Additionally, we created a new skin dataset by merging two skin datasets together, Skin-NotSunExposed-Suprapubic and Skin-SunExposed-Lowerleg, as an internal control. The code used to download and process these two datasets is hosted at https://github.com/reactome-idg/gather-app.We collected a dataset of 106 protein/gene pairwise relationship features to train a random forest model for predicting functional interactions of proteins. The majority of these features were obtained from gene coexpressions derived from bulk RNA-seq data obtained from the GTEx and TCGA projects. Tissue-specific gene expression data was downloaded from the GTEx portal, https://string-db.org/cgi/download) [https://downloads.thebiogrid.org/BioGRID/) [https://bioplex.hms.harvard.edu/interactions.php#datasets) [https://github.com/reactome-idg/fi-network-ml/tree/master/src/main/java/org/reactome/idg/ppi.Human protein-protein interactions were downloaded from StringDB  , BioGridioGRID/)  and BioPatasets) , 41. To https://string-db.org/cgi/download. For BioGrid, we acquired the following files in March 2020: BIOGRID-ORGANISM-Saccharomyces_cerevisiae_S288c-3.5.181.tab2.txt (yeast), BIOGRID-ORGANISM-Caenorhabditis_elegans-3.5.181.tab2.txt (worm), BIOGRID-ORGANISM-Drosophila_melanogaster-3.5.181.tab2.txt (fly), and BIOGRID-ORGANISM-Mus_musculus-3.5.181.tab2.txt (mouse). These files were downloaded from https://downloads.thebiogrid.org/BioGRID/. The non-human PPIs were loaded as pairs of UniProt identifiers and merged separately for each model organism species from the StringDB and BioGrid datasets. To map the model organism PPIs to human, we employed different mapping strategies. For yeast, worm, and fly, we used the panther orthologous mapping file, RefGenoeOrthologs.tar.gz, downloaded from ftp://ftp.pantherdb.org/ortholog/ in March 2019. We filtered this file to extract human genes using the command “grep ‘ĤUMAN’ RefGenomeOrthologs > HUMAN_RefGenomeOrthologs”. To map mouse PPIs to human PPIs, we utilized the ENSEMBL Compara protein families  to maximize the number of mappable PPIs between the two species.In addition to human protein-protein interactions (PPIs), we incorporated PPIs from model organism species, including yeast, worm, fly, and mouse, and subsequently mapped them to human for functional interaction prediction. The model organism PPIs were obtained from StringDB and BioGrid. From StringDB, we downloaded the following files in March 2020: 4932.protein.links.full.v11.0.txt (yeast), 6239.protein.links.full.v11.0.txt (worm), 7227.protein.links.full.v11.0.txt (fly), and 10090.protein.links.full.v11.0.txt (mouse). Corresponding mapping files were used for each organism: yeast.uniprot_2_string.2018.tsv, celegans.uniprot_2_string.2018.tsv, fly.uniprot_2_string.2018.tsv, and mouse.uniprot_2_string.2018.tsv. All of these files were obtained from https://maayanlab.cloud/Harmonizome/download. To integrate this data into our workflow, we ported the Python script at its download website into Java. We manually selected a subset of datasets that were likely to provide pathway-related information, while excluding datasets related to gene expressions or non-human data. For details on the selected datasets, please refer to the “harmonizome_datasets_annotations_062819.xlsx” file in Supplemental Results.Gene similarity data was downloaded from Harmonizome, https://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam32.0/ in December, 2018) and gene GO annotations were downloaded from the GO web site . Both the domain-domain interactions and GO annotations were loaded as gene or protein pairwise features and mapped to human gene symbols for compatibility.Protein-protein domain interactions were acquired from pFam  extracted from complexes and reactions in Reactome  using a method we developed previously to build the Reactome FI network  and calchttps://github.com/reactome-idg/fi-network-ml/blob/master/src/main/resources/harmonizome_selected_files.txt.For gene similarities obtained from Harmonizome, we employed an adaptive cutoff approach based on a methodology described by Iacono et al . This apSelectedFeatures_0415_2020.xlsx in Supplemental Results.In total we collected 106 gene or protein pairwise relationship features, which encompassed 31 cancer specific gene co-expression features from TCGA, 48 tissue specific co-expression features from GTEx, 20 gene similarity features from Harmonizome, 5 protein-protein interaction features from StringDB, BioGrid, and BioPlex, 1 protein domain-domain interaction feature from pFAM, and 1 biological process annotation sharing feature from GO. Further details and analysis results for these features can be found in The random forest model was trained using the RandomForestClassifier model from the scikit-learn Python package (version 0.23) with Python 3.8. To determine the optimal parameters for this model, we employed the gridsearch function in scikit-learn. The parameters explored in the grid search included class_weight , max_depth , max_features , min_samples_leaf , min_samples_split , and n_estimators . To conduct the grid search, we randomly selected 100 positive gene pairs representing functional interactions (FIs) and 10,000 negative gene pairs. These pairs were divided into 75% for training and 25% for validation, and this process was repeated 10 times.https://github.com/reactome-idg/fi-network-ml/blob/master/scripts/ml/fi_predictor.py.The final set of parameters used in the Random Forest model were as follows: bootstrap=True, ccp_alpha=0.0, class_weight=‘balanced’, criterion=‘gini’, max_depth=10, max_features=‘auto’, max_leaf_nodes=None, max_samples=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None, oob_score=False, random_state=42, verbose=0, warm_start=False. For further details on the implementation, please refer to the Python script available at To identify functionally relevant gene pairs, we utilized the random forest classifier trained in our study. We selected gene pairs with prediction scores greater than or equal to 0.8 as the set of predicted functional interactions (FIs). Next, we performed pathway enrichment analysis for each gene in the predicted FI set by using its FI partners as a gene set. We utilized human pathways from Reactome release 77  for this analysis. To assess the significance of pathway enrichment, we employed a Binomial test and calculated p-values. To account for multiple testing, we also computed adjusted p-values for individual genes using the Benjamini-Hochberg FDR  Procedure .To further analyze the impact of predicted FIs on pathways, we employed fuzzy logic models based on Boolean networks. These models were automatically converted from Reactome pathways. We adopted a previously developed approach used for calculating drug impact scores on pathways . Two simhttps://figshare.com/articles/dataset/Tabula_Sapiens_release_1_0/14267219?file=34701964. We downloaded the h5ad file, loaded it as an AnnData object using the scanpy Python package [We obtained scRNA-seq data for blood from the Tabula Sapiens project  via http package , and fil package , and the package  with spe package . The selhttps://ftp.ncbi.nlm.nih.gov/pubmed/baseline/, to obtain all PubMed indexed abstracts published by the end of 2021. We employed SentenceTransformer , a Python package, to embed the downloaded abstracts into 384-dimensional numeric vectors using a pre-trained BERT model called “all-MiniLM-L6-v2”. To retrieve abstracts related to a specific gene, we employed a simple text matching approach, collecting any abstracts that mentioned the gene’s name or its protein product’s name or synonyms. The gene’s and protein’s names and synonyms were obtained from UniProt’s website (https://www.uniprot.org/uniprot/?query=*&fil=reviewed%3Ayes+AND+organism%3A%22Homo+sapiens+%28Human%29+%5B9606%5D%22), selecting columns including Entry, Entry name, Protein names, Gene names (primary), and Gene names (synonym). To assess the similarities between Reactome pathways and abstracts, we applied the same BERT model to embed the text in the Summation instances annotated for pathways and reactions into numeric vectors. To determine the similarity between a pathway and an abstract, we first calculated cosine similarities using the cos_sim function in the SentenceTransfomer package between the abstract and the pathway and all events (pathways and reactions) annotated inside this pathway using their BERT embeddings and then took the mean of these similarities. For example, Pathway_1 has Sub_Pathway_1 annotated and Sub_Pathway_1 has a Reaction_1 annotated. To calculate the cosine similarity between Pathway_1 and Abstract_1, we calculated three cosine similarities for Pathway_1 and Abstract_1, Sub_Pathway_1 and Abstract_1, and Reaction_1 and Abstract_1 using their respective BERT embeddings, and then calculated the mean of these three similarities as the cosine similarity between Pathway_1 and Abstract_1. This process allowed us to capture the relationship between the higher-level pathway, its sub-pathways, and the annotated reactions within the context of the abstract. To calculate the annotation score for a gene and a pathway, we selected at most 1,000 abstracts having highest average cosine similarities to all Reactome pathways under analyzed, calculated the cosine similarities for individual abstracts to pathways and then took the mean of these similarities as the annotation score. All code utilized in this analysis was implemented in Python and is available in our GitHub repo, https://github.com/reactome-idg/fi-network-ml/tree/master/scripts/nlp.We downloaded the PubMed 2022 baseline via manual annotation of interacting pathways for 20 dark proteins.xlsx, in Supplemental Results.We randomly selected 20 dark proteins that have not been annotated in Reactome. For each protein, we searched PubMed using the names of the protein or its gene or the protein’s UniProt identifiers as keywords. We read the full text paper to determine if there was any experimental evidence to support the predicted interacting pathways for the protein. Further, we also checked GeneCards, UniProt and GO entries for any direct experimental evidence to support the predicted interacting pathways. The detailed annotation was provided in the file, https://spring.io, Release 4.3.10) with a MongoDB database  as the backend to store predicted functional interactions, pairwise features, interaction pathways and all related information. We also used a MySQL database for Reactome specific information (e.g. genes in pathways and pathway diagrams). To enhance the pathway widgets, we forked the original Reactome web application projects and added new features using GWT . To develop the homepage of the Reactome IDG web portal, we used Vuejs, a JavaScript framework  and plugins for Vuejs, including vuetify  for general user interfaces, vue-cytoscape  for network views, and vue-plotly  for plotting. For Java related software projects, we used maven  to manage dependencies while for JavaScript project, we used npm . All code is open sourced and available in GitHub repositories hosted at https://github.com/reactome-idg, including idg-pairwise-ws (https://github.com/reactome-idg/idg-pairwise-ws) for the RESTful APIs and idg-homepage (https://github.com/reactome-idg/idg-homepage) for the Reactome IDG homepage.We extended the Reactome web application by adding new RESTful APIs and enhancing the original web-based pathway visualization widgets. The development of the new RESTful API followed the Spring MVC framework (We used R (version 4.1.1) and Python (3.7 or 3.8) for statistical analysis and plot. All code is available in our GitHub repo.Supplement 1


Ixodes scapularis and Ixodes pacificus ticks. Surveillance for ticks and the pathogens they can transmit can inform local tick-borne disease risk and guide public health interventions. The objective of this article is to characterize passive and active surveillance of the main Lyme disease tick vectors in Canada in 2019 and the tick-borne pathogens they carry.The primary vectors of the agent of Lyme disease in Canada are Passive surveillance data were compiled from the National Microbiology Laboratory Branch and provincial public health data sources. Active surveillance was conducted in selected sentinel sites in all provinces. Descriptive analysis of ticks submitted and infection prevalence of tick-borne pathogens are presented. Seasonal and spatial trends are also described.I. scapularis  were submitted from all provinces except British Columbia and I. pacificus (n=691) were submitted in British Columbia and Alberta. No ticks were submitted from the territories. The seasonal distribution pattern was bimodal for I. scapularis adults, but unimodal for I. pacificus adults. Borrelia burgdorferi was the most prevalent pathogen in I. scapularis (18.8%) and I. pacificus (0.3%). In active surveillance, B. burgdorferi was identified in 26.2% of I. scapularis; Anaplasma phagocytophilum in 3.4% of I. scapularis, and Borrelia miyamotoi and Powassan virus in 0.5% or fewer of I. scapularis. These same tick-borne pathogens were not found in the small number of I. pacificus tested.In passive surveillance, specimens of This surveillance article provides a snapshot of the main Lyme disease vectors in Canada and their associated pathogens, which can be used to monitor emerging risk areas for exposure to tick-borne pathogens. Ixodes scapularis and Ixodes pacificus are tick vectors capable of transmitting several bacterial, viral and protozoan pathogens to humans , babesiosis (caused by the parasite Babesia microti), hard tick-borne relapsing fever (caused by the bacterium Borrelia miyamotoi) and Powassan virus (POWV) disease are emerging as diseases locally acquired within Canada ). Ixodada (  –4))). Cl)). ClIxoada )). –7)). Ixo)). –7)).ada )))). Cl)).–8)))). C)))). Cl)ada ( ( )).Lyme ada )). T)). TIxod0)). T)).ada ( ( –15))).Pa)).PaIxoderi ( ( )). Actieri )). T)). TIxodal. ( ( )) summaThis article uses two types of surveillance data from six different sources: 1) passive tick surveillance data from the National Microbiology Laboratory (NML) Branch of the Public Health Agency of Canada, the British Columbia Centre for Disease Control and Alberta Health ( ( )); and I. scapularis and I. pacificus ticks collected in Canada, although several other tick species were also collected. Ticks with an international location of acquisition, an imprecise location within Canada that could not be geocoded  or history of travel were excluded to create a dataset of locally acquired ticks. Over the years, passive tick surveillance programs have been discontinued in different jurisdictions, i.e. Nova Scotia, southwestern Québec (Montérégie) and eastern Ontario; however, the public continues to submit a relatively small number of ticks acquired in these jurisdictions directly to NML.In 2019, Saskatchewan, Manitoba, Ontario, Québec, Newfoundland and Labrador, New Brunswick, Nova Scotia and Prince Edward Island sent ticks to NML for testing of tick-borne pathogens  using methods described previously  for species identification ( ( )). Locasly )). T)). TI. ssly ( ( )) and Asly ( ( )), testI. scapularis and I. pacificus collected by drag sampling were included for analysis, although several other tick species were also collected.Active surveillance involves collection of ticks in the environment through drag sampling or through capture of host mammals that are examined for ticks. This method aims to identify where emerging tick populations are establishing )). F)). F18))This article collates data from CaLSeN, the New Brunswick Department of Health and the University of Ottawa. The CaLSeN used standardized methods to conduct dragging in 96 sites across all provinces ( ( )). The I. scapularis and I. pacificus were tested for tick-borne pathogens. Ticks collected by CaLSeN and by the province of New Brunswick were tested for A. phagocytophilum, B. microti, B. burgdorferi, B. miyamotoi and POWV (CaLSeN ticks only) at NML using methods previously described  assays described previously )),22))I. s)),22))I.sly ( ( )) using,22)),22)For passive tick surveillance, we calculated descriptive statistics for province of acquisition, tick species, instar , level of engorgement , host  and month of collection. For active tick surveillance, we calculated descriptive statistics for province of acquisition, tick species and instar . The probable location of acquisition for ticks was mapped using QGIS (version 3.8.1).For ticks submitted through passive surveillance, maximum likelihood estimates (MLE) of prevalence with 95% confidence intervals (CI) were calculated in Excel (version 16.0) using the PooledInfRate add-in (version 4.0) to account for pooled testing )). C)). C27))I. pacificus and I. scapularis ticks submitted from all provinces through passive surveillance  , followed by dogs .Data on tick instar, level of engorgement and host were available for 99.9%, 0% and 100% of I. pacificus and 99.4% of I. scapularis. Locally acquired ticks were submitted in every month of the year , but only 0.3% of I. pacificus (95% CI: 0.05–0.97). Other tick-borne pathogens and co-infections were less prevalent  than from single submissions   than those submitted from non-human hosts   than those submitted from human hosts . Both B. burgdorferi-infected I. pacificus ticks were from human hosts.4–17.13) . HoweverB. burgdorferi-infected tick submissions were within previously identified LD risk areas  . Newfoundland and Labrador, Nova Scotia and Québec all had higher infection prevalence of B. burgdorferi than the national average for I. scapularis  . The majI. scapularis and I. pacificus were found at 78 of 184 surveillance sites (range of ticks found: n=0–130). Ixodes scapularis  were found in Manitoba, Ontario, Québec, New Brunswick, Nova Scotia and Prince Edward Island, while I. pacificus (n=10) were found in British Columbia. Regarding the instar, 51.5%  of ticks were identified as nymphs, 29.5%  were adults and 19.0%  were larvae.In active surveillance, I. pacificus collected and 73.8%–98.3% of I. scapularis nymphs and adults collected, depending on the pathogen. No tick-borne pathogens were found in I. pacificus ), or frds ( ( )).Ixodes spp. ticks, for example, were often found in western Canada in the winter but were rarely infected  of exposure to ticks, which may or may not be infected with tick-borne pathogen(s). ted ( ( )). The bia ( ( )) and ibia ( ( )), as nbia ( ( )). Bimoada )),32))Ixod)),32))Ixres )). N)). NIxodres ( ( )), peakres ( ( )).B. burgdorferi among I. pacificus ticks in British Columbia (0.3%) was consistent with annual rates from 2002 to 2018 between 0.1 and 0.4% ( ( ((I. scapularis was lower (18.3%) than the 2018 minimum infection rate of 20.7% ). In M.7% ( ( )). In O.8% ( ( )). In Q015 ( ( )). InteI. scapularis being infected with at least one of the tick-borne pathogens tested was higher in multiple submissions than single submissions. As multiple submissions are indicators of tick establishment in a given area ), thisB. burgdorferi-infected ticks had probable locations of acquisition within LD risk areas. The LD risk areas are identified by the provinces using the methods described in the 2016 national LD case definition ) and asts ( ( )). PublIn active surveillance, there was geographic variability in infection prevalence, similar to findings from passive surveillance. Conducting standardized and consistent active surveillance across the country can help identify new LD-risk areas and detect other emerging tick-borne pathogens in known LD-risk areas, thereby informing local risk of exposure to TBD.B. burgdorferi was the most prevalent tick-borne pathogen in both passive and active surveillance, A. phagocytophilum, B. microti, B. miyamotoi and POWV were also detected. All provinces, however, had lower infection prevalence than hyper-endemic areas in the northeastern United States. For example, I. scapularis adults collected in Maine through passive surveillance had B. burgdorferi, A. phagocytophilum and B. microti infection prevalence of 42.4%, 11.1% and 6.5%, respectively ( ( ((While ely ( ( )).Ixodes spp. habitat in the future ). As cure )),,40)),Ixo)),,40)),les ( ( )).This inaugural article combining active and passive tick surveillance presents a national snapshot of tick vectors and their emerging associated pathogens. By integrating the two types of surveillance, the strengths and weaknesses of the individual systems are complemented. Whereas active surveillance is resource-intensive and therefore limited in geographic scope, passive surveillance programs can be implemented on a larger geographic scale; however, passive surveillance lacks specificity as it often collects adventitious ticks seeded by migratory birds, especially ticks collected from companion animal hosts which readily acquire ticks from the environment )).)).38))))There are several limitations to this study. Provincial passive surveillance programs, and the effort and timing of active surveillance, vary across Canada due to resource limitations or logistics. Passive tick surveillance has been discontinued or limited to specific hosts in several regions. Further, passive tick surveillance can be limited by public awareness, and geographic or host-specific biases in tick submissions )),43))42,4)),43))42I. scapularis and I. pacificus across Canada in varying amounts depending on location, including some ticks which were infected with tick-borne pathogen(s). Both passive and active tick surveillance have utility in signalling and confirming new LD risk areas, which can be used to inform public health authorities where environmental risk for LD occurs. This information is used to communicate the local risk of LD and TBD to the public as well as to healthcare workers. Continued surveillance will be crucial for monitoring any expansion of areas at risk of exposure to ticks and tick-borne pathogens, and to appropriately target public health interventions such as education and awareness campaigns towards at-risk areas.Passive and active surveillance identified both


Helicobacter pylori eradication is characterized by decreasing successful eradication rates. Although treatment failure is primarily associated with resistance to antibiotics, other unknown factors may influence the eradication outcome. This study aimed to assess the presence of the antibiotics resistance genes in H. pylori and the presence of Candida spp., which are proposed to be endosymbiotic hosts of H. pylori, in gastric biopsies of H. pylori-positive patients while simultaneously assessing their relationship. The detection and identification of Candida yeasts and the detection of mutations specific for clarithromycin and fluoroquinolones were performed by using the real-time PCR (RT-PCR) method on DNA extracted from 110 gastric biopsy samples of H. pylori-positive participants. Resistance rate to clarithromycin and fluoroquinolone was 52% and 47%, respectively. Antibiotic resistance was associated with more eradication attempts (p < 0.05). Candida species were detected in nine (8.18%) patients. Candida presence was associated with older age (p < 0.05). A high rate of antibiotic resistance was observed, while Candida presence was scarce, suggesting that endosymbiosis between H. pylori and Candida may not be a major contributing factor to the eradication failure. However, the older age favored Candida gastric mucosa colonization, which could contribute to gastric pathologies and microbiome dysbiosis. Helicobacter pylori is a Gram-negative, microaerophilic flagellated bacterium which is highly adjusted to the generally unfavorable conditions for microbial growth present in the human stomach. H. pylori is embedded in the gastric mucosa, where it locally adjusts the pH to a neutral value due to urease activity [H. pylori in the stomach, with colonization prevalence ranging from 35% in developed countries to a strikingly high prevalence of 80% in some developing countries [H. pylori carriers remain asymptomatic, H. pylori colonization is associated with several gastric pathologies, including gastritis, peptic ulcers, non-cardiac gastric adenocarcinoma and gastric mucosa-associated lymphoid tissue (MALT) lymphoma [H. pylori is recognized as a type 1 carcinogenic agent, responsible for ~90% of gastric cancer cases [activity ,2. It isountries . Althouglymphoma ,5. Todayer cases .H. pylori is treated with antibiotics, and according to the WHO Global guidelines, triple therapy, comprising of clarithromycin, amoxicillin and a proton-pump inhibitor (PPI) is the most commonly used first-line treatment for the eradication of H. pylori in low-resistance regions, but according to new Maastrich VI guideline treatment with PPI-clarithromycin-containing triple therapy without prior susceptibility testing should be abandoned when the clarithromycin resistance rate in the region is more than 15% [H. pylori strains has compromised the effectiveness of antibiotic therapy and decreased the rate of successful eradication, with the alarming increase of multidrug-resistant H. pylori strains [H. pylori from multiple centers across the USA and Europe, revealed an alarmingly high rate of clarithromycin resistance with a rate >15% in every region of the USA [Confirmed infection with than 15% ,8. Data than 15% . The datthan 15% . However strains ,12,13. R the USA .H. pylori eradication success is also influenced by a number of other factors. It is well established that PPIs enhance the effect of antibiotics by increasing the gastric pH and ultimately H. pylori eradication. Their effect is under influence of the drug-metabolizing enzyme CYP2C19 and its gene polymorphism [H. pylori resistance to the applied antibiotic therapy were proposed. Studies have shown that bacteria upon establishing certain cell density have the ability to produce extracellular substances, allowing them to form a biofilm. Biofilm provides a self-protective matrix that can make the bacteria up to 1000 times more resistant to antibiotics, and this mechanism could also potentially contribute to the failure of H. pylori eradication efforts [H. pylori behaves as a facultative intracellular bacterium, found inside eukaryotic cells including human gastric epithelial cells, and macrophages and dendritic immune cells [H. pylori inside the Candida yeasts’ vacuoles has been confirmed recently by fluorescence microscopy and H. pylori-specific genes and proteins have been isolated from the yeast cells [H. pylori inside the Candida yeast cells has been characterized as a protective adaptation to stressful conditions, including changes in pH values, antibiotics treatments, and nutrient-deficient environment [H. pylori, Candida cells are resistant to environmental stress and can survive temperatures above 40 °C and a pH in the range between 2 and 10 [Candida colonization of the gastric mucosa is associated with several gastric pathologies. Moreover, the co-existence of Candida species and H. pylori has been confirmed in several studies and a synergistic relationship between these two microorganisms in the pathogenesis of gastric diseases has been suggested [Candida and H. pylori, we hypothesized that Candida presence in the gastric compartment could contribute not only to H. pylori pathologies but also to the resistance to the applied antibiotic therapy.In addition to antibiotic resistance, morphism . Moreove efforts . Furtherne cells ,18. Intest cells ,18,19,20ironment ,21,22,23ironment . In cont2 and 10 ,17,18,19uggested ,24. BaseH. pylori has been considered the only true inhabitant of the human stomach. However, despite the harsh environmental conditions, the existence of a thriving and dynamic gastric microbial community has been proven [2–104 colony-forming units (CFU)/mL [2 CFU/mL [Candida represents the most frequently isolated genus of yeasts, with C. albicans as the most frequently identified species [Candida and H. pylori could facilitate the H. pylori resistance to eradication treatment, as Candida yeasts could serve as reservoirs for an easier transmission [Candida infection might contribute to an increase in H. pylori infection rates and might contribute to the challenges of its eradication.For a long time, n proven ,25,26,27(CFU)/mL . In addi2 CFU/mL ,25,28. C species ,21,29. Asmission . In thisH. pylori and Candida fungi on a cohort of 110 H. pylori-positive patients. In addition to Candida detection and species level identification, any possible relationship between Candida presence in different histopathological categories of biopsies, demographic characteristics of patients, number of eradications and the type of antibiotic resistance mutation detected in H. pylori strains, was assessed.To test the hypothesis in the present study we used molecular methods to test the frequency of concurrent infections with H. pylori-positive test was the inclusion criteria for the study. Demographic and clinical data of H. pylori-positive patients were recorded. Histological examinations were carried out in the Department of Pathohistology of the University Clinical Centre of Serbia. A semi-quantitative categorization of histopathological findings was performed and atrophy, inflammation and cellularity scores were assessed.A total of 123 patients with symptoms of dyspepsia recruited at the University Clinical Centre of Serbia were enrolled in the study. During gastroscopy, gastric tissue biopsy samples from the patients’ stomach antrum, angular incisura and corpus were obtained according to the updated Sydney classification of gastritis . The gasThe study was approved by the Ethics Committee of the University Clinical Centre of Serbia (number 788/11). All patients were informed regarding the aim of the study and provided written informed consent in accordance with the World Medical Association Declaration of Helsinki.Isolation of DNA from biopsies of the gastric mucosa was performed using the commercial kit, QIAmp DNA Mini , according to the manufacturer’s instructions.H. pylori was detected using the Giemsa stain  on the mucosal surface during the histology examination and by molecular testing. The presence of mutations in genes related to antibiotic resistance was detected for the following genes involved in the H. pylori resistance mechanisms: 23S, gyrA, gyr87 and gyr91.H. pylori and resistance to clarithromycin and fluoroquinolones was performed using the GenoType HelicoDR kit , according to the manufacturer’s instructions. Briefly, the highly specific region, as well as regions of DNA whose mutations lead to resistance to clarithromycin (CLA) and fluoroquinolones (gyrA), were amplified by RT-PCR, using biotin-labelled primers. Amplified products were then denatured and hybridized using specific oligonucleotides (probes) that are complementary to sequences characteristic of wild alleles (WT probes) and sequences of mutated alleles (MUT probes). Reverse hybridization was performed using a TwinCubator at a temperature of 45 °C. A denaturation solution was added to the amplified product (20 μL), after which the remaining steps were performed according to the manufacturer’s protocol  [Molecular detection of Germany) .Candida yeasts were detected using the RT-PCR protocol described in a study by Zhang et al. [® ep Gradient S realplex RT-PCR system (Eppendorf) and QuantiNovaTM SYBR® Green PCR Kit (Qiagen). For Candida yeasts detection, the following primers were used: 5.8S-1F5′-CAA CGG ATC TCT TGG TTC TC-3′ and 28S-1R 5′-CGG GTA GTC CTA CCT GAT TT-3′ [Candida species were identified based on the obtained and the reference melting curve profiles. Samples were tested for the presence of several Candida species, including C. albicans, C. tropicalis and C. parapsilosis. For positive controls, C. albicans ATCC24433, C. tropicalis ATCC23705 and C. parapsilosis ATCC24574 were used. Melting curve analysis was performed by gradually increasing the temperature from 65°C to 95 °C at the rate of 1.5 °C/min. The species-specific melting temperatures used for the identification of Candida species are shown in g et al. . In shorAT TT-3′ . CandidaCandida yeasts were detected using the RT-PCR protocol described in a study by Zhang et al. [® ep Gradient S realplex RT-PCR system (Eppendorf) and QuantiNovaTM SYBR® Green PCR Kit (Qiagen). For Candida yeasts detection, the following primers were used: 5.8S-1F5′-CAA CGG ATC TCT TGG TTC TC-3′ and 28S-1R 5′-CGG GTA GTC CTA CCT GAT TT-3′ [Candida species were identified based on the obtained and the reference melting curve profiles. Samples were tested for the presence of several Candida species, including C. albicans, C. tropicalis and C. parapsilosis. For positive controls, C. albicans ATCC24433, C. tropicalis ATCC23705 and C. parapsilosis ATCC24574 were used. Melting curve analysis was performed by gradually increasing the temperature from 65° to 95 °C at the rate of 1.5 °C/min. The species-specific melting temperatures used for the identification of Candida species are shown in g et al. . In shorAT TT-3′ . CandidaCandida colonization, presence of H. pylori gene mutations related to antibiotic resistance and demographic data of patients, as well as the number of eradications and the histopathological scores of biopsies. Non-parametric methods, Chi-square and Fisher’s Exact tests were used for data analysis with a level of significance of p < 0.05. The data were collected and analyzed using SPSS version 26.Statistical analyses were performed in order to assess the relationship between H. pylori detection results or incomplete information regarding histopathological criteria. For 110 confirmed H. pylori-positive patients, the demographic and clinical data were recorded. The age of participants in the analyzed cohort ranged between 21 and 91 years, with a median age of 56 years. The most reported gastric pathologies included gastritis , dyspepsia , peptic or duodenal ulcers  and gastroesophageal reflux disease (GERD) . Gastric biopsies of the participants were investigated, and samples were categorized according to cellularity, inflammatory and atrophy scores.Out of 123 patients in the cohort, biopsy samples of 13 patients were not analyzed in detail due to the negative Candida species were detected and identified using described real-time PCR method in nine (8.18%) participants , while C. tropicalis and C. parapsilosis were present in two (22.2%) samples each.icipants . At the Candida-positive and Candida-negative groups. Statistical analyses were performed by assessing the relationship between the distribution of Candida-positive and Candida-negative patients following different histopathological categories , demographic categories , number of eradication and the type of antibiotic resistance mutation detected in H. pylori strain . Moreover, all Candida-positive patients were older than 50 years, compared to the Candida-negative group in which 59 participants (58.4%) were >50 years old.A significantly older age was detected in the Candida-positive and Candida-negative groups, with no statistically significant difference between them, as presented in the Candida-positive (55.6%) and the Candida-negative groups (50.5%).The results of antibiotic resistance testing indicate high resistance rates to both clarithromycin and fluoroquinolone drugs in Candida species and any of the assessed histopathological categories, number of eradications, gender and the presence of H. pylori mutations (p > 0.05). The cellularity score of 2 was most frequently noted in both groups. Biopsy inflammatory scores of 1 and 2 were the most frequently observed, with the highest rate of inflammatory activity score of 1 in both Candida-positive and Candida-negative groups. Regarding the atrophy assessment, atrophy scores 0 and 1 were most commonly observed, with the majority of patients having an atrophy score of 1, 51% and 66.7% for Candida-negative and Candida-positive patients, respectively  patients, followed by dyspepsia in two (22.2%), gastroduodenitis in two (22.2%) and gastritis and duodenal ulcer in one patient each (11.1%). Other documented pathologies including sideropenic anemia, esophageal varices and diaphragmatic hernia were absent among Candida-positive patients. The most common diseases diagnosed in the Candida-negative group included gastritis, which was diagnosed in 39 patients, followed by gastroduodenitis diagnosed in 20 patients. Diagnosis of gastritis or duodenitis was more frequent among Candida-negative patients, and this was at the border of significance p = 0.056. Other diagnoses among Candida-negative patients were dyspepsia (17 patients), chronic gastritis (12 patients), duodenal ulcer (9 patients), gastroesophageal reflux (8 patients), hiatus hernia (8 patients), secondary sideropenic anaemia (5 patients) and gastroduodenitis (2 patients). A Chi-square analysis revealed no difference between Candida-positive and negative participants regarding the incidence of ulcers (p> 0.05).Concerning the pathologies, among gyrA, 23S, and gyr87 and gyr91 codon. Overall, the resistance gene mutations to clarithromycin and fluoroquinolone antibiotic classes were found in 57 (52.8%) and 52 (47.3%) samples, respectively (The presence of gene mutations associated with resistance to fluoroquinolones and clarithromycin was assessed for ectively .p < 0.05). In particular, significant difference in resistance rates was observed between patients with more than one eradication attempt, compared to the eradication-naïve (naïve) and patients with one episode of eradication, for fluoroquinolone, clarithromycin and dual resistance (p < 0.05). Moreover, higher antibiotics resistance rates were confirmed in patients with failed eradication attempts, for fluoroquinolones (p = 0.008) and clarithromycin (p = 0.002), compared to the naïve group. An overall dual antimicrobial resistance was detected in 33 from 110 (30%) participants. In addition to the higher incidence of detected mutations among participants with recurrent H. pylori infection, significantly higher cellularity, inflammation and atrophy scores were found in previously treated patients compared to the naïve participants (p < 0.05), as shown in Correlation analysis revealed a significant association between the resistance to antibiotics and the number of treatments, with a significantly higher incidence of 23S and gyrA mutations observed in previously treated participants, compared to the naïve group (p = 0.034), as presented in p = 0.008). Women also had a higher incidence of resistance to both drugs (p = 0.004). There was no significant difference between antibiotic resistance and other parameters, including age, Candida colonization and the presence of gastrointestinal inflammatory diseases  (p > 0.05).Interestingly, resistance to clarithromycin was associated with fluoroquinolone resistance (p = 0.001) and dual resistance (p = 0.007), compared to the patients with fluoroquinolone-sensitive H. pylori. No significant difference was observed for the resistance gene mutations and other pathohistological criteria (p > 0.05).Significantly higher atrophy scores were observed in patients with resistance to fluoroquinolones  are responsible for H. pylori’s resistance to macrolide and quinolone antibiotics, respectively [H. pylori antibiotics. The analysis of the correlation between the antibiotic resistance to clarithromycin and fluoroquinolone and the eradication status revealed a significantly higher frequency of mutations related to resistance in previously treated subjects, compared with the untreated group, p < 0.05, as presented in Considering an increasing prevalence of valuated . H. pyloapproach ,12,34,35ountries . A globaountries . In the  failure . The claapeutics ,39. Our ectively ,41. The H. pylori antimicrobial resistance to take place in the USA and Europe, a comparable and alarmingly high clarithromycin resistance rate was affirmed. Culture based, susceptibility testing revealed that all USA subregions and the majority of European countries have clarithromycin resistance rates above 15%  [H. pylori strains resistant to other antibiotics has increased the usage of levofloxacin and other quinolone drugs. Levofloxacin resistance was estimated to be significantly lower compared to clarithromycin, ranging from 5% in Australia and Italy to 15% in France and 16% in Spain [H. pylori resistant to clarithromycin and fluoroquinolones, respectively, showing a significant increase in resistance to antibiotic drugs . The most prevalent Candida species was C. albicans found in five Candida-positive patients, followed by C. tropicalis and C. parapsilosis identified in two patients each. Previous studies have identified C. albicans as the most frequently isolated fungus from the gastric mucosa while other Candida species have also been detected, including C. glabrata, C. parapsilosis, C. famata and C. tropicalis [In addition to the primary association of treatment failure with resistance to antibiotics, other factors may further influence the outcome of eradication, including the possible entry of ic cells . Intraceironment ,20,43,44nditions ,23,45. Rinvolved . The devsearched . To our rganisms . Similaropicalis ,48.n = 74, 67.3%), which provides further evidence of H. pylori as a causative factor in gastritis development [Karczewska and colleagues reported a significantly higher incidence of gastric ulcers in the group with H. pylori and Candida co-infection [Candida-positive and negative patients in the current study (p > 0.05). The present results indicate a lower frequency of gastritis or duodenitis in a group with detected Candida colonization at the border of significance (p = 0.056); it should be noted that a relatively small number of Candida-positive samples hampered a reliable statistical assessment. Compared to the study conducted by Zwolińska and colleagues, where fungal colonization was detected in 54.2% of participants with gastric ulcers and 10.3% of chronic gastritis cases, we observed a much lower incidence of gastric ulcers  in Candida-positive patients [Candida-positive patients in our cohort , followed by dyspepsia and gastroduodenitis , representing the variety of gastric pathologies connected with H. pylori colonization [The majority of patients in our study had been diagnosed with gastritis or duodenitis , which further supports previous findings that older age is a major predisposing factor for the fungal colonization of the stomach [Maastricht guidelines, a high dose of PPI is recommended for H. pylori eradication therapy and all H. pylori-positive patients should be treated with antibiotics [Candida colonization of the gastric mucosa [H. pylori eradication, PPIs are widely prescribed drugs for other indications. However, recent studies have proved that chronic PPI usage promotes perturbations in gastric microbial communities by altering the pH value in the stomach [Mottaghi and colleagues, where the use of ranitidine, pantoprazole and omeprazole was associated with a significantly increased risk of gastric candidiasis [ stomach . Accordiibiotics ,45; meanc mucosa . Besides stomach ,52. A codidiasis .H. pylori infection and intestinal microbiota dysbiosis [Candida glabrata and other unclassified fungi, were also found to be associated with H. pylori infection [H. pylori on gastric microbial communities, attempts at H. pylori eradication using several courses of antibiotics also affect the gastric microbiota and may result in gastric dysbiosis [H. pylori positive. We found no significant difference in resistance rates between Candida-positive and Candida-negative groups, indicating that yeast colonization alone does not have a major impact on the transmission of H. pylori resistance genes.Previous studies indicated a connection between ysbiosis . The altnfection . In addiysbiosis ,55. In tH. pylori to Candida species and the development of polymicrobial biofilms [Candida and H. pylori exacerbates the development of gastric diseases. The results of recent studies indicate the importance of other gastric microbial members in the pathogenesis of gastric inflammatory diseases. Notably, the existence of a significant correlation between gastric dysbiosis and aforementioned pathologies was observed, highlighting the importance of perturbations in the gastric microbiome in the etiopathology of gastric diseases [In addition to the endosymbiotic relationship, other forms of interactions between fungal and bacterial gastric community members have been observed, including the adherence of biofilms ,57. Howediseases ,27,28,53diseases ,58,59.H. pylori infection, a more responsible antibiotic stewardship should be implemented. Tailored therapy based on individual susceptibility testing has been proved to be more effective than empiric therapy  [H. pylori-antibiotics interrelationship is more intricate and tailored therapy does not always lead to eradication [Candida colonization which would enable resistance to an antibiotic susceptible H. pylori strain, by allowing it intracellular protection, leading to treatment failure. Latifi-Navid et al. and our unpublished data reveal ketoconazole as an agent which can additionally lead to successful eradication and can reduce the chance of recurrence of bacterial infection. However, further studies are warranted to elucidate the extent of their relationship [In order to combat a growing global antibiotic resistance and prevalence of regions) . Althougregions) . Assessidication . This cotionship .Candida yeast was assessed in a small cohort of H. pylori-positive patients, with an idea to elucidate reasons for H. pylori eradication failure. While there is an alarming level of antibiotic resistance , the co-occurrence of H. pylori and Candida species were found in only 8% of patients. In the analyzed cohort, the fungal colonization was associated with older age, suggesting that the age-related changes in physiological functions and microbiome composition favors Candida colonization of the stomach. Previous studies suggested that Candida could serve as a host facilitating a rescue of H. pylori under unfavorable conditions. Our results suggest that such phenomena could exist only in a small proportion of patients. A global increase in the prevalence of resistant H. pylori strains has resulted in an increased interest in H. pylori infection management strategies. New approaches, including vaccines, phytochemicals, secondary bacterial metabolites and probiotic strains are needed. Moreover, as studies of the mechanisms of H. pylori invasiveness and colonization revealed the facultative intracellular nature of bacteria, further research into the endosymbiotic relationship between H. pylori and Candida species is suggested.In this study, antibiotic resistance genes and colonization with


Helicobacter pylori is a gastric pathogen that infects nearly half of the global population and is recognized as a group 1 carcinogen by the Word Health Organization. The global rise in antibiotic resistance has increased clinical challenges in treating H. pylori infections. Biofilm growth has been proposed to contribute to H. pylori’s chronic colonization of the host stomach, treatment failures, and the eventual development of gastric diseases. Several components of H. pylori have been identified to promote biofilm growth, and several of these may also facilitate antibiotic tolerance, including the extracellular matrix, outer membrane proteins, shifted morphology, modulated metabolism, efflux pumps, and virulence factors. Recent developments in therapeutic approaches targeting H. pylori biofilm have shown that synthetic compounds, such as small molecule drugs and plant-derived compounds, are effective at eradicating H. pylori biofilms. These combined topics highlight the necessity for biofilm-based research in H. pylori, to improve current H. pylori-targeted therapeutic approaches and alleviate relative public health burden. In this review we discuss recent discoveries that have decoded the life cycle of H. pylori biofilms and current biofilm-targeted treatment strategies. Helicobacter pylori is a Gram-negative, spiral-shaped, bacterial pathogen that colonizes the gastric epithelium [H. pylori has been globally recognized as a high priority pathogen as it has been associated with various gastric diseases, including peptic ulcers, chronic gastritis [H. pylori infection have alarmingly been losing efficacy in regions with high infection burden [H. pylori was reported to disproportionately affect children in Asian, African, and European countries [H. pylori in biofilms, a low growth state, may substantially promote antibiotic resistance and persistence in the host stomach [H. pylori were initially observed in vitro to form water-insoluble biofilms which are defined as stationary aggregates of cells encased in extra polymeric substances (EPS) [H. pylori with biofilm state have also been observed in the gastric mucosa of patients with peptic ulcers [H. pylori biofilm growth in vitro and in vivo, elucidate supportive evidence of antibiotic tolerance and current developing biofilm-targeted anti-H. pylori strategies.ithelium ,2,3. H. astritis ,5, gastrastritis , and gasastritis ,8,9,10. n burden . Antibioountries , and in ountries . One cur stomach . H. pyloes (EPS) ,16. H. pc ulcers ,18. In tH. pylori biofilms consist of stationary aggregates of cells encased by an extracellular matrix composed of proteins [H. pylori biofilm formation starts from planktonic cells that adhere to either abiotic or biotic surfaces, leading to the formation of microcolonies with three-dimensional structures [H. pylori cells can cluster together as non-surface-attached aggregates, a form that has been recently observed and recognized as a biofilm format in other bacterial studies [H. pylori biofilm formation was found to occur optimally under conditions lacking nutrients, such as fetal bovine serum [proteins , extraceproteins , and polproteins . H. pyloructures ,23. Addi studies . Once adne serum ,26.H. pylori can form a microcolony network that adhered and grew between epithelial cell junctions on human cells [H. pylori biofilms consist of different cell shapes within one multicellular population. For example, both spiral and coccoid H. pylori cells were simultaneously observed from one gastric biopsy [H. pylori biofilm formation exhibits a similar multiple-step process, including bacterial adherence, biofilm assembly, mature biofilm formation, and dispersion ; serum commonly promotes planktonic growth but inhibits surfaces adherence [H. pylori surface adhesion as FBS is an undefined medium with a non-homogeneous mix of growth factors [H. pylori adhesion on gastric epithelial cell surface does not rely on the presence of FBS, suggesting that this bacterium may utilize a specialized mechanism for surface attachment [H. pylori surface adhesion is also strain-dependent [Adhesion is an essential process that initiates  biofilm ,33,34. Pal cells  and abioal cells . H. pylodherence . It rema factors . Interestachment ,37,38,39tachment . H. pyloependent . We focuH. pylori flagella play important roles in adherence and subsequent biofilm formation. They are made up of four primary components; basal body, hook, filament, and sheath [H. pylori motility but have been recently discovered to be involved in promoting surface adhesion and maintaining biofilm architecture [H. pylori to initiate biofilm [+ Mot−) due to deletion of a flagellar basal body gene motB, or lost flagella (Fla− Mot−) due to deletion of the flagellar basal body gene fliM [+ Mot− strains exhibited initial attachment defects on gastric cell surfaces [H. pylori biofilm formation.d sheath . Flagellitecture . Motilit biofilm . More inene fliM . More bisurfaces . These rH. pylori outer membrane proteins (OMPs), which can be on the cell surface or present in outer membrane vesicles (OMVs) along with virulence factors and eDNA [Another type of molecule shown to contribute to adherence are and eDNA ,43. OMPsand eDNA ,45.H. pylori has more than 60 OMPs coding genes [H. pylori biofilm formation, one of which is to promote surface adhesion as discussed [H. pylori OMPs facilitate both cell-to-cell and cell-to-abiotic surface adhesion in biofilms, based on the observation of OMV localization in H. pylori biofilms via scanning electron microscopy (SEM) imaging [ng genes , but notng genes . Beyond iscussed ,48. H. p imaging .H. pylori OMPs is anti-parallel β sheets that compose a β-barrel, highly stable pore-like structure; transmembrane domains of these proteins interact with host cell receptors [A common feature of eceptors , potentiH. pylori OMPs is the Hom family, a group of four proteins encoded by the following genes: homA, homB, homC, and homD [homA and homB were found to contribute H. pylori biofilm formation as well [H. pylori biofilm and relative pathology. The outer membrane protein homB , was recently associated with biofilm formation [H. pylori upregulates homB transcription via ArsRS, a two-component system, in the initial adherence and assembly phases of biofilm growth, but then levels fall back to those observed in planktonic cells after 72 h of incubation [One family of and homD . These pand homD . Interes as well , indicatormation . This prormation  and gastormation . H. pylocubation . This vahomD and homC genes, are both upregulated during H. pylori biofilm formation [H. pylori strains [HomA and HomB are composed mainly of β-sheets with cysteine resides on surface loops that help to form homodimers and indicate that they are potentially key to aggregation and biofilm formation . Variousormation . Polymor strains . These fH. pylori biofilm formation. An uncharacterized autotransporter, paralogous to VacA, vlpC, was found to cause a defect of H. pylori biofilm formation if disrupted [vlpC has been upregulated in some biofilms, further supporting this factor is important for H. pylori biofilm formation [The outer membrane protein autotransporter is also likely to play a role in regulating isrupted ,46. Specormation ,25.H. pylori biofilm regulation as well. AlpB, a Hop family member, was implicated in biofilm formation and antibiotic resistance, since the genetic deletion of alpB caused less H. pylori biofilm formation [H. pylori strains, it has recently been identified and investigated as a therapeutic target to eradicate H. pylori biofilm [A group of highly conserved laminin binding proteins of another OMP family, called Hop, has also been shown to be involved in ormation ,38. Sinc biofilm .These findings highlight the role played by OMPs and flagella at this stage, while also emphasizing that there is much to be discovered.H. pylori starts forming microcolonies or aggregates that are recognized as the pre-mature form of biofilm [H. pylori biofilms formation steps have been characterized using confocal microscopy. This work showed that H. pylori strain G27 assembles biofilms initially at the liquid air interface at 24 h, then assembles aggregates both at the liquid air interface and under the liquid air interface as the biofilm assembled; the distribution of EPS, visualized by staining, paralleled this growth trend [H. pylori biofilm structure, as discussed above [H. pylori biofilms were slowly assembled [H. pylori biofilm assemble at rates that are similar among different strains when calculating cumulative frequency and rate of formation [After surface adhesion,  biofilm ,25,55. M biofilm , and mor biofilm ,25. H. pth trend . SEM fured above ,25. Withssembled . Comparaormation . Additioormation .H. pylori biofilms observed in SEM images are flagellar filaments which were discovered to promote surface cohesion and cell-to-cell connections as mentioned above, together with pili formations sustain the biofilm structural integrity on both abiotic and biotic surfaces [The maximum biofilm mass can be observed after 3 days in vitro incubation ,57, and surfaces ,25.H. pylori strains and incubation conditions can differentially impact biofilm formation kinetics. H. pylori strains with strong and poor biofilm-forming abilities in tissue culture plates had consistent biomass accumulation rates during the intermediate assembly phases but had a variant cumulative biomass at the mature phase after 7 days of growth [H. pylori SS1 strain produced robust biofilms in relatively low FBS conditions after 3 days of growth on polystyrene plates, with most biofilm cells (~80%) being coccoid shaped [H. pylori G27 strain did not rely on low-serum conditions, as biofilm formation was not impaired even at standard culture media, with 10% FBS and produced biofilms with similar morphological features as SS1 [In the meantime, different f growth . In anotd shaped . Interess as SS1 .H. pylori was proposed as a response to the environmental stressors, but the underlying mechanism for this morphology is not fully characterized [H. pylori biofilms revealed that the strongest biofilm-producing cells show a dominance of coccoid forms unlike weak biofilm-producing cells which presented rod-shaped forms that were dominant in mature biofilms [In mature biofilms grown on abiotic surfaces , most cecterized . A recencterized . A morphbiofilms .H. pylori in the coccoid morphology is more tolerant to antibiotic exposure [H. pylori biofilm’s strong tolerance to antibiotics [H. pylori biofilms [atpC, atpE, and nifU [H. pylori biofilm formation on biotic surfaces [H. pylori and AGS cells for days, H. pylori biofilms were observed on the surfaces and between conjunctions AGS cells [H. pylori biofilm formation. Further studies are necessary to dissect whether different incubation conditions, such as serum concentration and incubation period may modulate H. pylori biofilm features.Interestingly, exposure  which alibiotics ,25. Viabbiofilms . Transcrsurfaces ,49. AfteGS cells ,61. InteGS cells . Other cGS cells , suggestH. pylori biofilms disperse after reaching optimal growth, indicated by a decrease in crystal violet staining after maximum growth has been reached [H. pylori biofilm dispersal, but some evidence suggests that H. pylori utilizes a quorum-sensing molecule, AI-2, as a signaling molecule to regulate biofilm generation and dispersion [H. pylori sensed by chemoreceptor TlpB [H. pylori through luxS gene in a cell density-dependent manner [H. pylori can efficiently control local density through AI-2 secretion. A later study suggested that AI-2 promoted H. pylori biofilm dispersion, as genetic deletion of the luxS in H. pylori significantly promoted its biofilm formation in comparison to isogeneic WT strain through the lacunarity and fractal dimension analysis [H. pylori biofilm dispersion by sensing and responding to AI-2, since chemotactic histidine kinase-deficient mutant ∆cheA exhibited similar biofilm phenotype as the ∆luxS mutant [H. pylori regulates biofilm maturation and dispersion.Like other bacterial biofilms,  reached ,25,56. Lspersion . AI-2 wator TlpB , and thit manner ,65, sugganalysis . The cheS mutant . FurtherH. pylori infections that has been exacerbated due to the COVID-19 pandemic, a recent consensus report states a need for consistent updates in clinical treatments, including effective testing and preventative measures for gastric illness [H. pylori infections [Due to the persistence of disease development in  illness . Globall illness , a compo illness , Chinese illness , and Can illness  consensufections .H. pylori infection, current clinical guidelines recommend a quadruple therapy that consists of bismuth, proton pump inhibitor (PPI) or potassium-competitive acid blocker and two different antibiotics  [H. pylori infections where quadruple therapy is used reported that metronidazole, levofloxacin, and clarithromycin resistance was 40–70%, 20–50%, and 20–50%, respectively [H. pylori-infected population, respectively; while those less commonly applied antibiotics exhibited relative lower resistance prevalence, including amoxicillin (5.2%), tetracycline (2.6%), and clarithromycin (9.1%) [H. pylori as a high priority research pathogen [In regions with a high prevalence in icillin) ,66,67,69icillin) . For exaectively . Metronin (9.1%) . In 2020n (9.1%) .Aside frH. pylori has either individually or populationally developed multidrug resistance [H. pylori strains isolated from a region with prevalent H. pylori infection that apply quadruple treatment found strains with dual resistance to metronidazole and levofloxacin (20.5%) and triple resistance to metronidazole, clarithromycin, and levofloxacin (~7%) [rdxA and frxA loci [gyrA and gyrB mutations [H. pylori isolates from Indonesian patients with gastritis (90.1%), peptic ulcer disease (8.9%), and gastric cancer (1%) and discovered that 93% of the isolates formed biofilms [H. pylori to acquire high antibiotic tolerance; therefore, the eradication of H. pylori biofilm is likely a key process for clinical therapy. Nevertheless, there are challenges in clinal therapies: (1) planktonic susceptibility of minimal inhibitory concentration (MIC) may not be a reliable indicator of Minimal Biofilm Eradication Concentration (MBEC) with certain antibiotics [H. pylori-infected patients [H. pylori treatment.A meta-analysis review based on global WHO regions reported that clarithromycin resistance decreased the efficacy empiric eradications to less than 80%; additionally, metronidazole resistance was observed in >27% strains and levofloxacin resistance in >14% strains from all surveyed WHO regions in 2018 . To counsistance . A studyin (~7%) . A studyin (~7%) . Clarithin (~7%) ; metronirxA loci ; levofloutations . These mutations . Intrigubiofilms . These sibiotics ,81,82; ppGpp-mediated stringent response may play an important role in regulating H. pylori biofilm formation [H. pylori, biofilm formation has also been suggested to regulations of several transcriptional regulators, such as fliA, flgR, hp1021, fur, nikR, and crdR [Accumulating evidence suggests that  sensing  and (p)p sensing , two com sensing ,40,50, a sensing . For exabiofilms . Similarormation . In addiand crdR .H. pylori antibiotic susceptibility. Currently, two major types of techniques are utilized, either bacterial viability-based or molecular-based technique. Each type exhibits certain advantages and disadvantages.To well serve for diagnostic and treatment purpose, several approaches have been commonly applied to examine H. pylori [H. pylori dilution method allows several stains to be tested simultaneously. However, it is noteworthy that the bacterial viability-based techniques employed planktonic cells, whose results do not naturally reflect the profile of biofilm cells.Bacterial viability-based techniques are the standard approach to determine bacterial antibiotic susceptibility and has been utilized to track increasing antibiotic resistance ,85, by m. pylori . Differe. pylori . For exaH. pylori by using molecular-based approaches [Escherichia coli [E. coli. A limitation of this approach is that we may not yet know all antibiotic resistance alleles. Currently, this approach has not yet applied in examining H. pylori, but it seems to be a promising one.As various antibiotic resistance mechanisms have been characterized, and the genetic elements have been identified, these discoveries promote to detect the presence of responsible antibiotic-resistant elements or susceptible elements in proaches ,89,90,91proaches . More improaches ,94,95,96proaches . This typroaches . There ahia coli . This apH. pylori antibiotic tolerance [H. pylori does not accurately reflect the concentration needed to eradicate H. pylori biofilm cells. For example, a clinical study compared antibiotic susceptibility of H. pylori isolates between the planktonic and biofilm growth and found that H. pylori biofilms was more capable of tolerating various antibiotics relative to planktonic H. pylori, including up to 1000-fold with amoxicillin, 31.25-fold with clarithromycin, 16-fold with levofloxacin, and 8-fold with metronidazole [H. pylori biofilms have exhibited several advantages in facilitating antibiotic tolerance. Studies have proposed the correlation between high biofilm formation capacity in H. pylori and the tolerance to clarithromycin, but not however, metronidazole or levofloxacin [H. pylori biofilms is not yet fully understood, several ideas have been proposed, including that bacterial cells are protected by the biofilm structure; conjugated bacterial cells within the biofilm increase the chance of genetic exchange. Below we dissect recent mechanisms of antibiotic tolerance employed by H. pylori biofilms.Biofilm formation may play a significant role in facilitating olerance . A phenonidazole . H. pylofloxacin . While tH. pylori biofilms are encased in an extracellular polymeric substance (EPS) matrix that maintains the structural integrity of the biofilm, promotes adhesion, and facilitates cell-to-cell interactions [H. pylori biofilms [H. pylori biofilms with Confocal Laser Scanning Microscopy (CLSM) [H. pylori biofilms can be stained and visualized via CLSM using BOBO-3 [H. pylori EPS as proteinase K treatment significantly causes dispersion of H. pylori biofilms and reduces antibiotic tolerance [H. pylori biofilm reduction [ractions . Proteinbiofilms . Immunofbiofilms . Polysacy (CLSM) ,55. The y (CLSM) ,25. EPS g BOBO-3 . Enzymatolerance ,25. Whileduction ,25.H. pylori biofilms. Removal of proteins, however, does sensitize H. pylori in biofilms to clarithromycin, although it was not demonstrated whether this is EPS or surface protein removal [H. pylori biofilm may be a highly significant target to effectively eradicate this bacterium [In addition to sustaining structural integrity, the EPS may reduce the efficacy of drugs from reaching the interior of the biofilm. EPS itself is minimally affected during antibiotic exposure , support removal . Therefoacterium .H. pylori cells, coccoid cells are more commonly found in H. pylori biofilm [H. pylori that contributes to antibiotic resistance and disease induction [H. pylori biofilms, like other bacteria, can sustain the slow growth state [H. pylori is transitioning to the coccoid morphology [Compared with the spiral shape that is commonly observed in planktonic  biofilm ,25. The nduction ,102. H. th state ,104. Prirphology  and has H. pylori’s cell wall have been documented to be upregulated in H. pylori biofilms and may contribute to the coccoid form and/or antibiotic tolerance. For example, UppS, a putative undecaprenyl pyrophosphate synthase, facilitates H. pylori cell wall peptidoglycan modification [uppS, resulted in a defective biofilm formation [A couple of genes that modify fication . Transpoormation . Some naormation ; howeverormation .H. pylori biofilm structure is peptidoglycan deacetylase (PdgA). The pgda gene was upregulated in H. pylori biofilms [H. pylori biofilm formation [H. pylori biofilm formation. In addition, PdgA may play an important role in maintaining H. pylori biofilm structure as the H. pylori ΔpgdA mutant is more susceptible to lysozyme exposure, an enzyme that cleaves the peptidoglycan of the bacterial cell wall [Another cell wall factor found to be important for maintaining biofilms , and wasbiofilms . Oxidatiormation , which iell wall .hp0421, encoding cholesteryl-α-glucoside transferase, was also found to regulate cellular morphology in biofilms [hp0421 deletion caused defects in maintaining spiral morphology, an increase in susceptibility to antibiotics and promoted cellular aggregation to form pronounced biofilms faster than the wild-type controls [H. pylori morphologies and are synchronous with affecting biofilm phenotypes and antibiotic tolerance reveal a key topic that should be investigated to further decode H. pylori biofilms.In addition, another gene biofilms ,113. TheH. pylori treatment [H. pylori reduces its metabolic activities in the biofilm to mitigate such detrimental effects, along with the trend shifting to coccoid cellular morphology [H. pylori transcriptomic study that suggests biofilm cells are less metabolically active than planktonic cells due to the downregulation of metabolic genes [H. pylori is also able to upregulate specific metabolic enzymes to resist certain natural substrates, functionally as antibiotics. For example, Combretum mole extracts, an acetone-containing plant commonly consumed in South Africa to alleviate gastric illness, have bactericidal effects on H. pylori [acxA is upregulated in the H. pylori biofilm, indicating the acetone carboxylases is expressed to potentially degrade acetone during gastric colonization. Additionally, acxA deletion resulted in a significant biofilm defect [acxA gene is under regulation of both two-component system under the ArsRS [H. pylori biofilm and promoting gastric gland colonization [crdR and arsR regulators were found to be upregulated in biofilms [crdR was found to be upregulated in biofilms on abiotic surfaces [arsR was found to be upregulated in strain 26695 biofilms grown on abiotic surfaces and AGS [acxA gene is mandatorily expressed and essential to maintain certain functions of H. pylori biofilm including protecting H. pylori in the host from acetone degradation.Growing bacterial cells are more easily targeted by certain types of antibiotics, such as ampicillin, that is selected as an essential component of triple therapy applications for reatment . Recentlrphology . A recenrphology . This obic genes . Interes. pylori . To tolem defect ; the acxhe ArsRS  and the he ArsRS , which anization ,119. Botbiofilms ,84,120; surfaces ,119 and H. pylori cell membrane and facilitate the multiple drugs external transportation [H. pylori biofilms. Several efflux pumps coding genes, including Hp605 (hefA), Hp971 (hefD), Hp1327 (hefG), Hp1489, Hp1118, Hp1174 (gluP), HP0939, HP0497, and HP0471 (KefB), were found to be expressed in both planktonic and biofilm cells, suggesting that efflux pump is essential during H. pylori life cycles [H. pylori antibiotics tolerance [gluP, HP1165 (associated with tetracycline resistance), and hefA were significantly upregulated in biofilms relative to planktonic cells [hefA [hefD, hefG, and HP1489 were found to be particularly upregulated in biofilms from a clarithromycin-resistant strain TK1402 [gluP expression was found to be regulated by H. pylori stringent response and genetic deletions of gluP cause a biofilm defect and increased susceptibility to different types of antibiotics [H. pylori strains isolated from Nigeria while no association with hefG was detected [hefD and hefA have both recently been associated with multidrug resistance in clinical, these findings support the perspective that H. pylori utilizes biofilm growth to survive under antibiotic exposure and efflux pumps are a key contributor.Efflux pumps are commonly located on the ortation . Efflux ortation ,82,122 we cycles ,122,123.olerance . HPG27_7ic cells ,122. hefls [hefA ,121, hefibiotics . Additiodetected . CumulatH. pylori causes various gastric diseases, approaches are being developed to efficiently eradicate this bacterium. Here, we summarize several approaches based on the anti-biofilm treatments including synthetic compounds, natural compounds, and small molecule drugs.Since chronic infection with Antimicrobial peptides (AMPs) are promising alternatives to antibiotics for combating biofilm infections. One of the advantages of using AMPs is that these molecules are also less likely to induce resistance in bacteria than antibiotics because they target multiple components within the bacterial cell. These small peptides can penetrate the extracellular matrix that surrounds biofilm cells and thus target the bacteria directly.H. pylori in vitro and in vivo [10 reduction in bacterial counts in stomach tissues compared with an untreated mice group [alpA and alpB mRNA, both factors play a role in H. pylori adhesion and biofilms as mentioned above [Another antimicrobial peptide was also recently investigated, Cbf-K16, the Cathelicidin-like peptide showed good antimicrobial activity against clarithromycin- and amoxicillin-resistant  in vivo . In a moce group . Interesed above ,38,125.The antimicrobial peptide MSI-78A, also known as Pexiganan, is a 22-amino acid peptide Magainin-2 analogue, and was reported to have antibacterial activity in solution ,126. WheH. pylori, individually or synergistically with host antimicrobial peptides [H. pylori biofilms were treated with synthetic peptides IDR-1018 and DJK-5, it became more susceptible to the host-derived anti-microbial peptides [H. pylori strains  suggested that H. pylori utilizes a stringent response at low pH or with poor nutrients to produce significant amounts of ppGpp [H. pylori contains an enzyme called SpoT, a (p)ppGpp synthase, and hydrolase, whose genetic deletion causes a defective biofilm phenotype and an increased susceptibility to antibiotics [H. pylori biofilms and were observed to not affect viability of planktonic bacterial viability; biofilm assembly, however, was inhibited only by DJK-5 (dose-dependent). In contrast, IDR-1018 reduced mature H. pylori biofilms without affecting the bacterial viability within the biofilm matrix [H. pylori in the form of biofilms and that H. pylori utilizes mechanisms in biofilms homologous to other bacterial species affected by the same peptides [Several synthetic peptides were also applied and have been shown to promote biofilm dispersion in peptides . For inspeptides . In addipeptides  and IDR-peptides . Both DJpeptides ,129. Priof ppGpp . H. pyloibiotics . DJK-5 am matrix . These fpeptides .H. pylori. Some extractions have been found to be particularly effective in eradicating H. pylori by specifically targeting biofilm stability.Extractions from natural resources such as plants and other bacteria are commonly applied to treat various microbial infections, including H. pylori biofilm. Probiotic Lactobacillus fermentum UCO-979C was previously found to play a role in inhibiting H. pylori biofilm formation [Lactobacillus plantarum LN66 cell-free supernatant (CFS) can weaken H. pylori biofilm formation, an effect monitored by SEM and confocal laser scanning microscopy (CLSM) [Streptomyces armeniacus previously identified as antibacterial agents against Gram-positive pathogens [H. pylori as well by inhibiting H. pylori biofilm formation in a dose-dependent manner. In a mouse model to study multidrug-resistant H. pylori, dual therapy with ARM1 and omeprazole showed efficient killing efficacy, comparable to the standard triple therapy, and induced negligible toxicity against normal tissues [H. pylori activity than metronidazole [Probiotics can inhibit bacterial biofilms and thus play an auxiliary role in bacterial antibiotic therapy. As documented, the effects of different probiotic strains may play a varied role in restricting certain bacterial biofilms, including ormation . Furthery (CLSM) . Probioty (CLSM) ,133. Anoathogens . Armenia tissues . Moreovenidazole . All theH. pylori infection. For example, Antractylodes lancea volatile oils were recently found to inhibit H. pylori biofilm formation. This oil complex also exhibits a robust ability to reduce H. pylori virulence factor CagA translocation into host cells, a finding observed in a cell culture infection model [H. pylori biofilm stability. Phytochemicals from Acorus calamus, Colocasia esculenta Vitex trifolia, Azadirachta indica A. Juss exhibited a significant effect on inhibiting H. pylori biofilm formation as well [Acorus calamus exhibited the highest H. pylori anti-biofilm activity via a dose-dependent pattern [Azadirachta indica A. Juss) were also previously shown to have bactericidal properties and several other Neem tree phytochemicals  and were tested for toxicity towards H. pylori but only Nimbolide was found to kill both planktonic and biofilm H. pylori without having hemolytic activity; Nimbolide was effective towards the nine strains of H. pylori tested in a time and dose-dependent manner under various stressful growth conditions and metabolic activities [H. pylori biofilm in both in vitro and in vivo studies when combined with omeprazole as a dual therapy, even more efficiently compared with the standard triple therapy approach; more interestingly, this compound exhibited negligible toxicity against normal tissues, indicating the potential in its clinical application [Hibiscus rosa sinensis L. flower) also showed properties of inhibiting biofilms and bactericidal effects on drug-resistant H. pylori strains [Extracted organic products from plants are also important to treat bacterial infections. A variety of materials have been found to efficiently restrict on model . Additio as well ,138. Amotivities . Dihydrolication . Extract strains . Alginat strains .H. pylori, even in biofilm forms. Although not all these drugs have been tested in in vivo conditions, it is essential to investigate their potential as anti-H. pylori agents given that their effectiveness has already been confirmed in vitro.These recent findings open exciting possibilities for discovering natural compounds that effectively target and eliminate H. pylori biofilms by inhibiting an enzyme required for biofilm growth, carbonic anhydrase [H. pylori biofilm by enhancing the encapsulation of a given antibiotic, such as clarithromycin, to reduce biofilm viability and structural integrity more efficiently via bypassing the mucus layer and the EPS of the H. pylori biofilm [N-acylhomoserine lactonase silver nanoparticles (aka nanodrugs) in inhibiting H. pylori quorum-sensing system, potentially combats H. pylori biofilm formation [H. pylori biofilm formation when synergized with other antibiotics, like metronidazole [H. pylori biofilms in vitro and in vivo [Pseudomonas aeruginosa [Coptis chinensis that is proposed to enhance the efficacy of triple therapy for H. pylori infections [H. pylori and their biofilms [H. pylori biofilm and provides a feasible strategy to eradicate H. pylori infection.Various small molecule-based compounds that facilitate traditionally applied antibiotics, have been found to be effective at treating bacterial infections. These compounds include both organic and inorganic monomers or polymers that target bacterial essential enzymes, pathways, or structure. For example, carvacrol and thymol were found to inhibit nhydrase . Lipid p biofilm . A folloormation . Additionidazole ,143. Nan in vivo ,55; RHL ruginosa  and berbbiofilms . All theH. pylori, and its biofilm formation mechanisms. Notably, studies employing clinically isolated strains have played a crucial role in advancing our understanding and have paved the way for the development of promising biofilm-based approaches for eradicating H. pylori.In conclusion, the scientific community has made considerable strides in unraveling the intricate nature of the gastric chronic pathogen, H. pylori, and studying the effect of treatment candidates. Future studies are required to comprehensively evaluate the efficiency of recently proposed treatments on H. pylori eradication. These investigations are expected to extend beyond in vitro experiments and encompass comprehensive animal models and rigorous clinical trials. By conducting such studies, we can obtain a more accurate assessment of the therapeutic potential of these proposed treatments and their impact on both the host and the pathogen.Nevertheless, the complexity of in vivo environment and the limitation of current developed techniques cause difficulty of characterizing in vivo bacterial biofilm, including H. pylori, and study the effects of treatment candidates. Future studies will need to go beyond in vitro experiments and incorporate comprehensive animal models and rigorous clinical trials. Through conducting such studies, we can obtain a more accurate assessment of the therapeutic potential of these proposed treatments and their impact on both the host and the pathogen.However, the complexity of the in vivo environment and the limitations of current developed techniques make it challenging to characterize bacterial biofilm within host, including H. pylori may employ in response to biofilm-targeting therapies would be instrumental in designing more robust treatment strategies.Furthermore, it is important to explore the long-term effects of these novel approaches to ensure their safety and efficacy in real-world scenarios. Additionally, investigating potential resistance mechanisms that H. pylori biofilm formation and developing potential eradication approaches, further research is necessary to evaluate the pharmacological effects, efficacy, and safety of these treatments in animal models and clinical trials. By addressing these research gaps, we can bring us closer to achieving more effective and personalized strategies for combating H. pylori infection and its associated complications.In conclusion, while significant progress has been made in understanding