# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 5100 | 0 | 0.9973 | DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes. Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interactions are achieved by key genes and proteins. Methods for intraspecific prediction are rare since almost all existing methods consider only interactions at the species and genus levels. Moreover, most strains in existing databases contain only partial genome information because whole-genome information for species is difficult to obtain. Here, we propose a new approach for interaction prediction by constructing new features from key genes and proteins via the application of K-means sampling to select high-quality negative samples for prediction. Finally, we develop DeepPBI-KG, a corresponding prediction tool based on feature selection and a deep neural network. The results show that the average area under the curve for prediction reached 0.93 for each strain, and the overall AUC and area under the precision-recall curve reached 0.89 and 0.92, respectively, on the independent test set; these values are greater than those of other existing prediction tools. The forward and reverse validation results indicate that key genes and key proteins regulate and influence the interaction, which supports the reliability of the model. In addition, intraspecific prediction experiments based on Klebsiella pneumoniae data demonstrate the potential applicability of DeepPBI-KG for intraspecific prediction. In summary, the feature engineering and interaction prediction approaches proposed in this study can effectively improve the robustness and stability of interaction prediction, can achieve high generalizability, and may provide new directions and insights for rapid phage screening for therapy. | 2024 | 39344712 |
| 9182 | 1 | 0.9973 | Harnessing CRISPR/Cas9 in engineering biotic stress immunity in crops. There is significant potential for CRISPR/Cas9 to be used in developing crops that can adapt to biotic stresses such as fungal, bacterial, viral, and pest infections and weeds. The increasing global population and climate change present significant threats to food security by putting stress on plants, making them more vulnerable to diseases and productivity losses caused by pathogens, pests, and weeds. Traditional breeding methods are inadequate for the rapid development of new plant traits needed to counteract this decline in productivity. However, modern advances in genome-editing technologies, particularly CRISPR/Cas9, have transformed crop protection through precise and targeted modifications of plant genomes. This enables the creation of resilient crops with improved resistance to pathogens, pests, and weeds. This review examines various methods by which CRISPR/Cas9 can be utilized for crop protection. These methods include knocking out susceptibility genes, introducing resistance genes, and modulating defense genes. Potential applications of CRISPR/Cas9 in crop protection involve introducing genes that confer resistance to pathogens, disrupting insect genes responsible for survival and reproduction, and engineering crops that are resistant to herbicides. In conclusion, CRISPR/Cas9 holds great promise for advancing crop protection and ensuring food security in the face of environmental challenges and increasing population pressures. The most recent advancements in CRISPR technology for creating resistance to bacteria, fungi, viruses, and pests are covered here. We wrap up by outlining the most pressing issues and technological shortcomings, as well as unanswered questions for further study. | 2025 | 40663257 |
| 8400 | 2 | 0.9972 | Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BACKGROUND: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. METHODS: In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l(2)-regularized logistic regression model. RESULTS: The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. CONCLUSIONS: The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways. | 2018 | 29954330 |
| 8403 | 3 | 0.9972 | Uncovering virulence factors in Cronobacter sakazakii: insights from genetic screening and proteomic profiling. The increasing problem of antibiotic resistance has driven the search for virulence factors in pathogenic bacteria, which can serve as targets for the development of new antibiotics. Although whole-genome Tn5 transposon mutagenesis combined with phenotypic assays has been a widely used approach, its efficiency remains low due to labor-intensive processes. In this study, we aimed to identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a pathogenic bacterium known for causing severe infections, particularly in infants and immunocompromised individuals. By employing a combination of genetic screening, comparative proteomics, and in vivo validation using zebrafish and rat models, we rapidly screened highly virulent strains and identified two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats. Proteomic profiling revealed upregulated proteins upon knockout of rcsA and treR, including FabH, GshA, GppA, GcvH, IhfB, RfaC, MsyB, and three unknown proteins. Knockout of their genes significantly weakened bacterial virulence, confirming their role as potential virulence factors. Our findings contribute to understanding the pathogenicity of C. sakazakii and provide insights into the development of targeted interventions and therapies against this bacterium.IMPORTANCEThe emergence of antibiotic resistance in pathogenic bacteria has become a critical global health concern, necessitating the identification of virulence factors as potential targets for the development of new antibiotics. This study addresses the limitations of conventional approaches by employing a combination of genetic screening, comparative proteomics, and in vivo validation to rapidly identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a highly pathogenic bacterium responsible for severe infections in vulnerable populations. The identification of two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats and the proteomic profiling upon knockout of rcsA and treR provides novel insights into the mechanisms underlying bacterial virulence. The findings contribute to our understanding of C. sakazakii's pathogenicity, shed light on the regulatory pathways involved in bacterial virulence, and offer potential targets for the development of novel interventions against this highly virulent bacterium. | 2023 | 37750707 |
| 9220 | 4 | 0.9972 | Pathogen virulence genes: Advances, challenges and future directions in infectious disease research (Review). Pathogens, including bacteria, viruses and fungi, employ virulence genes to invade their hosts, circumvent immunity and induce diseases. The present review examines the categorization and regulatory mechanisms of virulence genes and their co‑evolution with antimicrobial resistance. The present review focused on the fimbrial adhesion H adhesion gene of Escherichia coli, the spike protein gene of severe acute respiratory syndrome coronavirus 2 and the enhanced filamentous growth protein 1 (EFG1) morphological transition gene of Candida albicans, as well as their roles in host adhesion, immune evasion and tissue damage. Application of technologies, including multi‑omics integration, artificial intelligence and CRISPR‑based genome editing, is discussed in the context of precision diagnostics, targeted therapy and vaccine development. By elucidating pathogen adaptation dynamics and host‑pathogen interactions, the present review offers a basis for reducing the global burden of drug‑resistant infections through improved surveillance and personalized interventions. | 2025 | 40849821 |
| 9554 | 5 | 0.9972 | A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction. | 2022 | 35272349 |
| 9076 | 6 | 0.9972 | ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/. | 2021 | 33495705 |
| 8167 | 7 | 0.9971 | Metal complexes against multidrug-resistant bacteria: recent advances (2020-present). The increasing prevalence of multidrug-resistant (MDR) bacterial infections worldwide represents a critical challenge to contemporary healthcare, with high mortality rates attributed primarily to biofilm formation and the widespread dissemination of antibiotic resistance genes. Metal complexes have emerged as promising candidates for combating resistant pathogens owing to their distinctive multi-target mechanisms. These compounds demonstrate dual functionality by effectively penetrating bacterial biofilms while simultaneously exerting antimicrobial effects through multiple pathways, including the production of reactive oxygen species (ROS) and interference with essential metal homeostasis. The growing inadequacy of conventional antibiotics against resistant infections necessitates the development of novel metal-based antimicrobial agents with low resistance propensity, high efficacy, and minimal toxicity profiles. The clinical validation of metallodrugs like auranofin provides a crucial foundation for designing next-generation anti-MDR therapeutics. Notably, complexes of gold (Au), silver (Ag), copper (Cu), gallium (Ga), iridium (Ir), and ruthenium (Ru) demonstrate multifaceted mechanisms of action through selective targeting of bacterial resistance mechanisms. These attributes enable them to provide a strategic framework for developing next-generation metal-based antibacterials. This review systematically summarizes the recent advances (2020-present) in the design and application of the complexes of these six metals against MDR bacteria, emphasizing their structural motifs, antimicrobial potency, and mechanistic insights. The presented insights provide novel approaches to combat the intensifying global challenge of antibiotic resistance. | 2025 | 41091096 |
| 8163 | 8 | 0.9971 | Green materials science and engineering reduces biofouling: approaches for medical and membrane-based technologies. Numerous engineered and natural environments suffer deleterious effects from biofouling and/or biofilm formation. For instance, bacterial contamination on biomedical devices pose serious health concerns. In membrane-based technologies, such as desalination and wastewater reuse, biofouling decreases membrane lifetime, and increases the energy required to produce clean water. Traditionally, approaches have combatted bacteria using bactericidal agents. However, due to globalization, a decline in antibiotic discovery, and the widespread resistance of microbes to many commercial antibiotics and metallic nanoparticles, new materials, and approaches to reduce biofilm formation are needed. In this mini-review, we cover the recent strategies that have been explored to combat microbial contamination without exerting evolutionary pressure on microorganisms. Renewable feedstocks, relying on structure-property relationships, bioinspired/nature-derived compounds, and green processing methods are discussed. Greener strategies that mitigate biofouling hold great potential to positively impact human health and safety. | 2015 | 25852659 |
| 5099 | 9 | 0.9971 | A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria. Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options. | 2024 | 39816256 |
| 9077 | 10 | 0.9971 | The PLSDB 2025 update: enhanced annotations and improved functionality for comprehensive plasmid research. Plasmids are extrachromosomal DNA molecules in bacteria and archaea, playing critical roles in horizontal gene transfer, antibiotic resistance, and pathogenicity. Since its first release in 2018, our database on plasmids, PLSDB, has significantly grown and enhanced its content and scope. From 34 513 records contained in the 2021 version, PLSDB now hosts 72 360 entries. Designed to provide life scientists with convenient access to extensive plasmid data and to support computer scientists by offering curated datasets for artificial intelligence (AI) development, this latest update brings more comprehensive and accurate information for plasmid research, with interactive visualization options. We enriched PLSDB by refining the identification and classification of plasmid host ecosystems and host diseases. Additionally, we incorporated annotations for new functional structures, including protein-coding genes and biosynthetic gene clusters. Further, we enhanced existing annotations, such as antimicrobial resistance genes and mobility typing. To accommodate these improvements and to host the increase plasmid sets, the webserver architecture and underlying data structures of PLSDB have been re-reconstructed, resulting in decreased response times and enhanced visualization of features while ensuring that users have access to a more efficient and user-friendly interface. The latest release of PLSDB is freely accessible at https://www.ccb.uni-saarland.de/plsdb2025. | 2025 | 39565221 |
| 9216 | 11 | 0.9971 | Mitigating Antibiotic Resistance: The Utilization of CRISPR Technology in Detection. Antibiotics, celebrated as some of the most significant pharmaceutical breakthroughs in medical history, are capable of eliminating or inhibiting bacterial growth, offering a primary defense against a wide array of bacterial infections. However, the rise in antimicrobial resistance (AMR), driven by the widespread use of antibiotics, has evolved into a widespread and ominous threat to global public health. Thus, the creation of efficient methods for detecting resistance genes and antibiotics is imperative for ensuring food safety and safeguarding human health. The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) systems, initially recognized as an adaptive immune defense mechanism in bacteria and archaea, have unveiled their profound potential in sensor detection, transcending their notable gene-editing applications. CRISPR/Cas technology employs Cas enzymes and guides RNA to selectively target and cleave specific DNA or RNA sequences. This review offers an extensive examination of CRISPR/Cas systems, highlighting their unique attributes and applications in antibiotic detection. It outlines the current utilization and progress of the CRISPR/Cas toolkit for identifying both nucleic acid (resistance genes) and non-nucleic acid (antibiotic micromolecules) targets within the field of antibiotic detection. In addition, it examines the current challenges, such as sensitivity and specificity, and future opportunities, including the development of point-of-care diagnostics, providing strategic insights to facilitate the curbing and oversight of antibiotic-resistance proliferation. | 2024 | 39727898 |
| 8628 | 12 | 0.9971 | Biofertilizer microorganisms accompanying pathogenic attributes: a potential threat. Application of biofertilizers containing living or dormant plant growth promoting bacterial cells is considered to be an ecofriendly alternative of chemical fertilizers for improved crop production. Biofertilizers opened myriad doors towards sustainable agriculture as they effectively reduce heavy use of chemical fertilizers and pesticides by keeping soils profuse in micro and macronutrients, regulating plant hormones and restraining infections caused by the pests present in soil without inflicting environmental damage. Generally, pathogenicity and biosafety testing of potential plant growth promoting bacteria (PGPB) are not performed, and the bacteria are reported to be beneficial solely on testing plant growth promoting characteristics. Unfortunately, some rhizosphere and endophytic PGPB are reported to be involved in various diseases. Such PGPB can also spread virulence and multidrug resistance genes carried by them through horizontal gene transfer to other bacteria in the environment. Therefore, deployment of such microbial populations in open fields could lead to disastrous side effects on human health and environment. Careless declaration of bacteria as PGPB is more pronounced in research publications. Here, we present a comprehensive report of declared PGPB which are reported to be pathogenic in other studies. This review also suggests the employment of some additional safety assessment protocols before reporting a bacteria as beneficial and product development. | 2022 | 35221573 |
| 9557 | 13 | 0.9971 | Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge. The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians' diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles. | 2022 | 35625299 |
| 9552 | 14 | 0.9971 | Addressing antibiotic resistance: computational answers to a biological problem? The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance. | 2023 | 37031568 |
| 8554 | 15 | 0.9971 | Nanomaterial-Enhanced Hybrid Disinfection: A Solution to Combat Multidrug-Resistant Bacteria and Antibiotic Resistance Genes in Wastewater. This review explores the potential of nanomaterial-enhanced hybrid disinfection methods as effective strategies for addressing the growing challenge of multidrug-resistant (MDR) bacteria and antibiotic resistance genes (ARGs) in wastewater treatment. By integrating hybrid nanocomposites and nanomaterials, natural biocides such as terpenes, and ultrasonication, this approach significantly enhances disinfection efficiency compared to conventional methods. The review highlights the mechanisms through which hybrid nanocomposites and nanomaterials generate reactive oxygen species (ROS) under blue LED irradiation, effectively disrupting MDR bacteria while improving the efficacy of natural biocides through synergistic interactions. Additionally, the review examines critical operational parameters-such as light intensity, catalyst dosage, and ultrasonication power-that optimize treatment outcomes and ensure the reusability of hybrid nanocomposites and other nanomaterials without significant loss of photocatalytic activity. Furthermore, this hybrid method shows promise in degrading ARGs, thereby addressing both microbial and genetic pollution. Overall, this review underscores the need for innovative wastewater treatment solutions that are efficient, sustainable, and scalable, contributing to the global fight against antimicrobial resistance. | 2024 | 39591087 |
| 5118 | 16 | 0.9970 | Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction. | 2022 | 35134132 |
| 8256 | 17 | 0.9970 | Revolutionizing Tomato Cultivation: CRISPR/Cas9 Mediated Biotic Stress Resistance. Tomato (Solanum lycopersicon L.) is one of the most widely consumed and produced vegetable crops worldwide. It offers numerous health benefits due to its rich content of many therapeutic elements such as vitamins, carotenoids, and phenolic compounds. Biotic stressors such as bacteria, viruses, fungi, nematodes, and insects cause severe yield losses as well as decreasing fruit quality. Conventional breeding strategies have succeeded in developing resistant genotypes, but these approaches require significant time and effort. The advent of state-of-the-art genome editing technologies, particularly CRISPR/Cas9, provides a rapid and straightforward method for developing high-quality biotic stress-resistant tomato lines. The advantage of genome editing over other approaches is the ability to make precise, minute adjustments without leaving foreign DNA inside the transformed plant. The tomato genome has been precisely modified via CRISPR/Cas9 to induce resistance genes or knock out susceptibility genes, resulting in lines resistant to common bacterial, fungal, and viral diseases. This review provides the recent advances and application of CRISPR/Cas9 in developing tomato lines with resistance to biotic stress. | 2024 | 39204705 |
| 6664 | 18 | 0.9970 | Addressing the global challenge of bacterial drug resistance: insights, strategies, and future directions. The COVID-19 pandemic underscored bacterial resistance as a critical global health issue, exacerbated by the increased use of antibiotics during the crisis. Notwithstanding the pandemic's prevalence, initiatives to address bacterial medication resistance have been inadequate. Although an overall drop in worldwide antibiotic consumption, total usage remains substantial, requiring rigorous regulatory measures and preventive activities to mitigate the emergence of resistance. Although National Action Plans (NAPs) have been implemented worldwide, significant disparities persist, particularly in low- and middle-income countries (LMICs). Settings such as farms, hospitals, wastewater treatment facilities, and agricultural environments include a significant presence of Antibiotic Resistant Bacteria (ARB) and antibiotic-resistance genes (ARG), promoting the propagation of resistance. Dietary modifications and probiotic supplementation have shown potential in reshaping gut microbiota and reducing antibiotic resistance gene prevalence. Combining antibiotics with adjuvants or bacteriophages may enhance treatment efficacy and mitigate resistance development. Novel therapeutic approaches, such as tailored antibiotics, monoclonal antibodies, vaccines, and nanoparticles, offer alternate ways of addressing resistance. In spite of advancements in next-generation sequencing and analytics, gaps persist in comprehending the role of gut microbiota in regulating antibiotic resistance. Effectively tackling antibiotic resistance requires robust policy interventions and regulatory measures targeting root causes while minimizing public health risks. This review provides information for developing strategies and protocols to prevent bacterial colonization, enhance gut microbiome resilience, and mitigate the spread of antibiotic resistance. | 2025 | 40066274 |
| 5101 | 19 | 0.9970 | Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria. | 2025 | 40671232 |