Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics—Current State and Whole Genome Sequencing Implementation Perspectives

Antibiotics, Год журнала: 2023, Номер 12(4), С. 781 - 781

Опубликована: Апрель 19, 2023

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions deaths annually. The rapid spread AMR across continents warrants systematic changes in healthcare routines and protocols. One fundamental issues with lack diagnostic tools pathogen identification detection. Resistance profile often depends on culturing thus may last up several days. This contributes misuse antibiotics viral infection, use inappropriate antibiotics, overuse broad-spectrum or delayed infection treatment. Current DNA sequencing technologies offer potential develop that can provide information a few hours rather than However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited routine lab use. In this review, we give an overview burden healthcare, describe current screening methods, perspectives how be used diagnostics. Additionally, discuss common steps data analysis, currently available pipelines, analysis. Direct, culture-independent has complement culture-based methods clinical settings. there need minimum set standards terms evaluating results generated. machine learning algorithms regarding phenotype detection (resistance/susceptibility antibiotic).

Язык: Английский

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Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation

BMC Biology, Год журнала: 2024, Номер 22(1)

Опубликована: Авг. 15, 2024

Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance human microbiome in relation to health. Identifying microbes associated with diseases is crucial for early disease diagnosis advancing precision medicine. Considering that information about changes microbial quantities under fine-grained states helps enhance a comprehensive understanding overall data distribution, this study introduces MSignVGAE, framework predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs graph variational autoencoder model noisy association extends multi-scale concept representation capabilities. A novel strategy propagating networks addresses heterogeneity consistency among nodes connected by edges. Additionally, we utilize idea denoising handle noise similarity feature information, which overcome biases fused data. represents as heterogeneous node features. The multi-class classifier XGBoost utilized predict between microbes. achieves AUROC AUPR values 0.9742 0.9601, respectively. Case studies on three demonstrate can effectively capture distribution leveraging information.

Язык: Английский

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GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier

BMC Bioinformatics, Год журнала: 2023, Номер 24(1)

Опубликована: Фев. 2, 2023

Abstract As new drug targets, human microbes are proven to be closely related health. Effective computational methods for inferring potential microbe-drug associations can provide a useful complement conventional experimental and will facilitate research development. However, it is still challenging work predict interactions or drugs, since the number of known very limited at present. In this manuscript, we first constructed two heterogeneous networks based on multiple measures similarity microbe-disease-drug associations, respectively. And then, established feature matrices drugs through concatenating various attributes drugs. Thereafter, after taking these as inputs two-layer graph attention network, obtained low dimensional representations separately. Finally, integrating with form convolutional neural network respectively, novel model named GACNNMDA was designed possible scores pairs. Experimental results show that predictive performance superior existing advanced methods. Furthermore, case studies well-known demonstrate effectiveness well. Source codes supplementary materials available at: https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA

Язык: Английский

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AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

Journal Of World Science, Год журнала: 2023, Номер 2(10), С. 1681 - 1695

Опубликована: Окт. 30, 2023

In the realm of healthcare, artificial intelligence (AI) emerges as a transformative force, reshaping established practices and offering unprecedented advancements. This comprehensive analysis delves into multifaceted ways AI is revolutionizing focusing on its capabilities, inherent challenges, crucial ethical complexities entwined in application. The challenge lies balancing transparency accountability amid intricate algorithms, particularly concerning interpretability AI-generated insights. explores dilemmas tied to patient autonomy evolving responsibilities healthcare providers. It advocates for open dialogue among systems, patients, professionals, navigating delicate balance between innovation welfare. article emphasizes imperative robust frameworks regulations governing implementation healthcare. investigation concludes by exploring AI's potential applications envisioning improved medical procedures, drug discoveries, remote monitoring, diagnostic enhancements. To harness power while safeguarding interests, collaboration data scientists, policymakers, ethicists paramount. abstract encapsulates profound shifts has initiated underscoring vital need addressing regulatory arising with integration. Ultimately, it portrays holistic view role highlighting revolutionize care, practices, entire landscape.

Язык: Английский

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Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Pharmaceutics, Год журнала: 2021, Номер 13(12), С. 2187 - 2187

Опубликована: Дек. 17, 2021

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical rewards. However, the manufacturing ODFs does not adhere to contemporary paradigms personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, three-dimensional (3D) printing machine learning (ML) were employed provide quality control checks ODFs. Direct ink writing (DIW) was able fabricate complex ODF shapes, with thicknesses less than 100 µm. ML algorithms explored classify according their active ingredient, by using near-infrared (NIR) spectrums. A supervised model linear discriminant analysis found 100% accuracy in classifying subsequent partial least square algorithm applied verify dose, where coefficient determination 0.96, 0.99 0.98 obtained paracetamol, caffeine, theophylline, respectively. Therefore, it concluded that combination 3D printing, NIR can result rapid production verification Additionally, vision tool used automate vitro testing. These collective digital technologies demonstrate potential workflow.

Язык: Английский

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Enterorenal crosstalks in diabetic nephropathy and novel therapeutics targeting the gut microbiota

Acta Biochimica et Biophysica Sinica, Год журнала: 2022, Номер 54(10), С. 1406 - 1420

Опубликована: Сен. 21, 2022

The role of gut-kidney crosstalk in the progression diabetic nephropathy (DN) is receiving increasing concern. On one hand, decline renal function increases circulating uremic toxins and affects composition gut microbiota. other intestinal dysbiosis destroys epithelial barrier, leading to increased exposure endotoxins, thereby exacerbating kidney damage by inducing systemic inflammation. Dietary inventions, such as higher fiber intake, prebiotics, probiotics, postbiotics, fecal microbial transplantation (FMT), engineering bacteria phages, are potential microbiota-based therapies for DN. Furthermore, novel agents, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors, sodium-dependent glucose transporter-2 (SGLT-2) may affect DN partly through In current review, we mainly summarize evidence concerning axis advancement discuss targeting microbiota, expecting provide new insight into clinical treatment

Язык: Английский

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Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

Briefings in Bioinformatics, Год журнала: 2022, Номер 23(3)

Опубликована: Фев. 16, 2022

Abstract In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs diseases. This paper provides comprehensive overview advances prediction correlations diseases from biological models. Firstly, we introduced widely used datasets relevant identification relationships detail. And then, divided series lot representative computing models into five major categories including network, matrix factorization, completion, regularization artificial neural network for in-depth discussion comparison. Finally, analysed possible challenges opportunities this research area, at same time outlined some suggestions further improvement predictive performances as well.

Язык: Английский

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Polysaccharides extracted from larvae of Lucilia sericata ameliorated ulcerative colitis by regulating the intestinal barrier and gut microbiota

International Journal of Biological Macromolecules, Год журнала: 2024, Номер 270, С. 132441 - 132441

Опубликована: Май 16, 2024

Язык: Английский

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A graph neural network approach for predicting drug susceptibility in the human microbiome

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108729 - 108729

Опубликована: Июль 1, 2024

Язык: Английский

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The triple interactions between gut microbiota, mycobiota and host immunity

Critical Reviews in Food Science and Nutrition, Год журнала: 2022, Номер 63(33), С. 11604 - 11624

Опубликована: Июль 1, 2022

The gut microbiome is mainly composed of microbiota and mycobiota, both which play important roles in the development host immune system, metabolic regulation, maintenance intestinal homeostasis. With increasing awareness pathogenic essence infectious, immunodeficiency, tumor-related diseases, interactions between bacteria, fungi, immunity have been shown to directly influence disease process or final therapeutic outcome, collaborative antagonistic relationships are commonly found bacteria fungi. Interventions represented by probiotics, prebiotics, engineered fecal transplantation (FMT), drugs can effectively modulate triple interactions. In particular, traditional probiotics Bifidobacterium Lactobacillus next-generation Akkermansia muciniphila Faecalibacterium prausnitzii showed a high enrichment trend patients with response inflammation remission tumor immunotherapy, predicts potential medicinal value these beneficial microbial formulations. However, there bottlenecks all interventions that need be broken. Meanwhile, further unraveling underlying mechanisms "triple interactions" model guide precise ultimately improve efficiency on modulation, thus indirectly improving anti-inflammatory immunotherapy effects.Gut mycobiota significantly pathology efficacy cooperative manner.Probiotics spp. highly enriched implies potential.

Язык: Английский

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