J Fungi (Basel). 2025 Feb 4;11(2):116. doi: 10.3390/jof11020116.

ABSTRACT

Allergic bronchopulmonary aspergillosis (ABPA) is a well-known complication in children and young people with cystic fibrosis (CF) and without treatment causes structural lung damage. We performed a longitudinal observational study to identify clinical risk factors for ABPA in a cohort of children and young people with CF aged 8 to 17 years at baseline. Anonymised annual review UK CF Registry data from 2009 to 2019 for patients aged 8-17 years in 2009 were collected, with lung transplant recipients excluded. Baseline characteristics are presented for the whole group and cross-sectional comparisons made according to the presence of ABPA or not in 2009. Longitudinal analysis from 2009 to 2019 was completed on the group without ABPA in 2009 to identify predictors for the subsequent development of ABPA using a complementary log-log regression model. In 2009, there were 1612 patients, of which 1420 were ABPA-negative and 192 ABPA-positive. Aspergillus colonisation (p = 0.01) and IV antibiotic use (p < 0.0001) were associated with having ABPA in 2009. Longitudinal analysis of the group without ABPA in 2009 identified male gender, younger age, lower lung function, Pseudomonas aeruginosa infection, and Aspergillus colonisation to be significantly associated with the development of ABPA (p < 0.0001). Ivacaftor was significantly associated with reduced ABPA (OR 0.46, p = 0.01) but not lumacaftor/ivacaftor (OR 0.64, p = 0.28). Chronic oral macrolide use was significantly associated with increased risk of development of ABPA (OR 1.30, p < 0.0001). This study shows that lower lung function, Aspergillus colonisation, and Pseudomonas aeruginosa infection in children with CF were associated with the development of ABPA, highlighting the need for enhanced surveillance in these patients. This is the first study to show a protective association of ivacaftor and ABPA.

PMID:39997410 | DOI:10.3390/jof11020116

Curr Issues Mol Biol. 2025 Feb 11;47(2):119. doi: 10.3390/cimb47020119.

ABSTRACT

Cystic fibrosis (CF) is a recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein, a chloride and bicarbonate channel localized on the plasma membrane of epithelial cells. Over the last three decades, high-throughput screening assays have been extensively employed in identifying drugs that target specific defects arising from CFTR mutations. The two main categories of such compounds are potentiators, which enhance CFTR gating by increasing the channel's open probability, and correctors, which improve CFTR protein folding and trafficking to the plasma membrane. In addition to these, other investigational molecules include amplifiers and stabilizers, which enhance the levels and the stability of CFTR on the cell surface, and read-through agents that promote the insertion of correct amino acids at premature termination codons. Currently, four CFTR modulators are clinically approved: the potentiator ivacaftor (VX-770), either as monotherapy or in combination with the correctors lumacaftor (VX-809), tezacaftor (VX-661), and elexacaftor (VX-445). Among these, the triple combination VX-445/VX-661/VX-770 (marketed as Trikafta® in the US and Kaftrio® in Europe) has emerged as the most effective CFTR modulator therapy to date, demonstrating significant clinical benefits in phase III trials for patients with at least one F508del CFTR allele. Despite these advancements, the mechanisms of action and binding sites of these modulators on CFTR have only recently begun to be elucidated. A deeper understanding of these mechanisms could provide essential insights for developing more potent and effective modulators, particularly in combination therapies. This narrative review delves into the mechanism of action, binding sites, and combinatorial effects of approved and investigational CFTR modulators, highlighting ongoing efforts to broaden therapeutic options for individuals with CF.

PMID:39996840 | DOI:10.3390/cimb47020119

Eur Radiol Exp. 2025 Feb 25;9(1):29. doi: 10.1186/s41747-024-00548-9.

ABSTRACT

BACKGROUND: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.

METHODS: Accelerated iMRI was performed using dual surface coils positioned around the area of resection. A DL model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. The evaluation was performed on imaging material from 40 patients imaged from Nov 1, 2021, to June 1, 2023, who underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two neuroradiologists and one neurosurgeon using a 1-to-5 Likert scale (1, nondiagnostic; 2, poor; 3, acceptable; 4, good; and 5, excellent), and the favored reconstruction variant.

RESULTS: The DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for readers 1, 2, and 3, respectively. For the evaluation metrics, the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for readers 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal.

CONCLUSION: DL shows promise in allowing for high-quality reconstructions of iMRI. The neuroradiologists noted an improvement in the perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to CS, while the neurosurgeon preferred the CS reconstructions across all metrics.

RELEVANCE STATEMENT: DL shows promise to allow for high-quality reconstructions of iMRI, however, due to the challenging setting of iMRI, further optimization is needed.

KEY POINTS: iMRI is a surgical tool with a challenging image setting. DL allowed for high-quality reconstructions of iMRI. Additional optimization is needed due to the challenging intraoperative setting.

PMID:39998750 | DOI:10.1186/s41747-024-00548-9

J Safety Res. 2024 Dec;91:354-365. doi: 10.1016/j.jsr.2024.10.002. Epub 2024 Oct 9.

ABSTRACT

INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore, this study aims to propose a deep learning model-based multi-label risk factor recognition (MRFR) framework that automatically recognizes multiple potential material and human risk factors at construction sites. The research answers the following questions: How can a deep learning model be developed and optimized to recognize and classify multiple material and human risk factors automatically and concurrently at construction sites, and how can the decision-making process of the model be understood and improved for practical application in preemptive safety management?

METHODS: Data comprising 14,605 instances of eight types of material and human risk factors were collected from construction sites. Multiple risk factors can occur concurrently; thus, an optimal model for multi-label recognition of possible risk factors was developed.

RESULTS: The MRFR framework combines material and human risk factors into a single label while achieving satisfactory performance with an F1 score of 0.9981 and a Hamming loss of 0.0008. The causes of mispredictions by MRFR were analyzed by interpreting the decision basis of the model using visualization.

CONCLUSION: This study found that the model must have sufficient capacity to detect multiple risk factors. Performance degradation in MRFR is primarily due to difficulties recognizing visual ambiguities and a tendency to focus on nearby objects when perspective is involved.

PRACTICAL APPLICATIONS: This study contributes to safety management knowledge by developing a model to recognize multi-label material and human risk factors. Furthermore, the results can be used as guidelines for data collection methods and model improvement in the future. The MRFR framework can be used as an algorithm to recognize risk factors preemptively and automatically at real-world construction sites.

PMID:39998535 | DOI:10.1016/j.jsr.2024.10.002

Invest Ophthalmol Vis Sci. 2025 Feb 3;66(2):63. doi: 10.1167/iovs.66.2.63.

ABSTRACT

PURPOSE: To compare the prediction accuracy of brachial-ankle pulse wave velocity (baPWV) from color fundus photographs (CFPs) using different deep learning models.

METHODS: This retrospective study analyzed the data of 696 participants whose baPWVs and CFPs were obtained during medical checkups. Arteriolar and venular probability maps, which were automatically calculated from the CFPs based on our modified deep U-net, Hokkaido University retinal vessel segmentation (HURVS) model, were applied as channel attention to retinal vessel location information to predict baPWV. The baPWV prediction parameters consisted of predicted baPWVs from a single-input model using CFPs only and from a three-input model using CFPs, and arteriolar and venular probability maps. The single- and three-input models adopted a common depth-wise net and were separately pretrained and trained with fivefold cross-validation. These baPWV prediction parameters were corrected using multiple regression equations with age, sex, and systolic blood pressure and were defined as single- and three-input regression-predicted baPWVs. The main outcome measures were the correlation coefficients between true baPWV and the baPWV prediction parameters.

RESULTS: The correlation coefficient with true baPWVs was higher for the three-input predicted baPWVs (R = 0.538) than for the single-input predicted baPWVs (R = 0.527). After regression, the three-input, regression-predicted baPWVs (R = 0.704) had the highest prediction accuracy, followed by the single-input, regression-predicted baPWVs (R = 0.692).

CONCLUSIONS: The three-input model predicted true baPWVs with high accuracy. This improved prediction accuracy by channel attention to the arteriolar and venular probability maps based on the HURVS model confirmed that arterioles and venules are relevant regions for baPWV prediction.

PMID:39998460 | DOI:10.1167/iovs.66.2.63

Radiology. 2025 Feb;314(2):e240427. doi: 10.1148/radiol.240427.

ABSTRACT

Background Lutetium 177 (177Lu) prostate-specific membrane antigen (PSMA) radioligand therapy is a novel treatment option for metastatic castration-resistant prostate cancer. Evidence suggests nephrotoxicity is a delayed adverse effect in a considerable proportion of patients. Purpose To identify predictive markers for clinically significant deterioration of renal function in patients undergoing 177Lu-PSMA-I&T radioligand therapy. Materials and Methods This retrospective study analyzed patients who underwent at least four cycles of 177Lu-PSMA-I&T therapy between December 2015 and May 2022. Total kidney volume (TKV) at 3 and 6 months after treatment was extracted from CT images using TotalSegmentator, a deep learning segmentation model based on the nnU-Net framework. A decline in estimated glomerular filtration rate (eGFR) of 30% or greater was defined as clinically significant, indicating a higher risk of end-stage renal disease. Two-sided t tests and Mann-Whitney U tests were used to compare baseline nephrotoxic risk factors, changes in eGFR and TKV, prior treatments, and the number of 177Lu-PSMA-I&T cycles between patients with and without clinically significant eGFR decline at 12 months. Threshold values to differentiate between these two patient groups were identified using receiver operating characteristic curve analysis and the Youden index. Results A total of 121 patients (mean age, 76 years ± 7 [SD]) who underwent four or more cycles of 177Lu-PSMA-I&T therapy with 12 months of follow-up were included. A 10% or greater decrease in TKV at 6 months predicted 30% or greater eGFR decline at 12 months (area under the receiver operating characteristic curve, 0.90 [95% CI: 0.85, 0.96]; P < .001), surpassing other parameters. Baseline risk factors (ρ = 0.01; P = .88), prior treatments (ρ = -0.06; P = .50), and number of 177Lu-PSMA-I&T cycles (ρ = 0.08; P = .36) did not correlate with relative eGFR percentage decrease at 12 months. Conclusion Automated TKV assessment on standard-of-care CT images predicted deterioration of renal function 12 months after 177Lu-PSMA-I&T therapy initiation in metastatic castration-resistant prostate cancer. Its better performance than early relative eGFR change highlights its potential as a noninvasive marker when treatment decisions are pending. © RSNA, 2025 Supplemental material is available for this article.

PMID:39998377 | DOI:10.1148/radiol.240427

Toxins (Basel). 2025 Feb 9;17(2):78. doi: 10.3390/toxins17020078.

ABSTRACT

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom of Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools and promising candidates for therapeutic development. However, traditional conotoxin classification and functional characterization remain labor-intensive, necessitating the increasing adoption of computational approaches. In particular, machine learning (ML) techniques have facilitated advancements in sequence-based classification, functional prediction, and de novo peptide design. This review explores recent progress in applying ML and deep learning (DL) to conotoxin research, comparing key databases, feature extraction techniques, and classification models. Additionally, we discuss future research directions, emphasizing the integration of multimodal data and the refinement of predictive frameworks to enhance therapeutic discovery.

PMID:39998095 | DOI:10.3390/toxins17020078

Tomography. 2025 Jan 27;11(2):13. doi: 10.3390/tomography11020013.

ABSTRACT

BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thickness has an adverse effect on the accuracy of the pulmonary nodule volumetry.

METHODS: We propose a deep learning based super-resolution technique to generate thin-slice CT images from thick-slice CT images. Analysis of the lung nodule volumetry and categorization accuracy was performed using commercially available AI-based lung cancer screening software.

RESULTS: The accuracy of pulmonary nodule categorization increased from 72.7 percent to 94.5 percent when thick-slice CT images were converted to generated-thin-slice CT images.

CONCLUSIONS: Applying the super-resolution-based slice generation on thick-slice CT images prior to automatic nodule evaluation significantly increases the accuracy of pulmonary nodule volumetry and corresponding pulmonary nodule category.

PMID:39997996 | DOI:10.3390/tomography11020013

Toxics. 2025 Feb 14;13(2):137. doi: 10.3390/toxics13020137.

ABSTRACT

The evaluation of air pollution is a critical concern due to its potential severe impacts on human health. Currently, vast quantities of data are collected at high frequencies, and researchers must navigate multiannual, multisite datasets trying to identify possible pollutant sources while addressing the presence of noise and sparse missing data. To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. Here, we report a combined approach involving the Self-Organizing Map (SOM) algorithm, Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to disentangle multiannual, multisite data in a single elaboration without previously separating the sites and years. The approach proved to be valid, allowing us to detect the site peculiarities in terms of pollutant sources, the variation in pollutant profiles during years and the outliers, affording a reliable interpretation.

PMID:39997952 | DOI:10.3390/toxics13020137

Nurs Rep. 2025 Feb 4;15(2):55. doi: 10.3390/nursrep15020055.

ABSTRACT

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices.

PMID:39997791 | DOI:10.3390/nursrep15020055

Metabolites. 2025 Feb 14;15(2):132. doi: 10.3390/metabo15020132.

ABSTRACT

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation.

PMID:39997757 | DOI:10.3390/metabo15020132

Metabolites. 2025 Feb 6;15(2):101. doi: 10.3390/metabo15020101.

ABSTRACT

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.

PMID:39997726 | DOI:10.3390/metabo15020101

Lung. 2025 Feb 25;203(1):35. doi: 10.1007/s00408-025-00789-4.

ABSTRACT

PURPOSE: The natural history of IPF remains unpredictable despite antifibrotic treatment. In addition, some patients discontinue treatment due to the occurrence of adverse events. To date, no data exist on either the effect of long-term treatment or predictors of treatment response. In the present study, we aim to evaluate the functional trajectory of IPF patients treated with antifibrotics for at least three years and to establish predictors of treatment response.

METHODS: This multicenter study enrolled long-term survivors IPF patients provided they had stopped treatment for no longer than one month during at least three-year study period. Based on the absolute decline of FVC%predicted (pred.) observed during the 3-year treatment and normalized per year, patients were defined as progressors (≥ 5%) or non-progressors (< 5%).

RESULTS: We identify 172 IPF patients who completed three years of antifibrotic treatment with no interruption. The 27% of these IPF patients progressed despite complete adherence to treatment. Progressors were more likely to be non-smokers compared to non-progressors, with higher occurrence of diarrhea and with a more preserved lung function at diagnosis. FVC %pred. and liters at diagnosis, a greater FVC decline in the 1-st year of follow up, being non-smokers, and complaining of diarrhea over treatment are independent predictors of progression.

CONCLUSION: Almost one third of IPF patients adherent to three years of antifibrotics experience progression. A functional decline at first year of treatment despite preserved lung function at diagnosis, non-smoking status, and occurrence of diarrhea over treatment are independent predictors of disease progression.

PMID:39998625 | DOI:10.1007/s00408-025-00789-4

J Fungi (Basel). 2025 Jan 29;11(2):102. doi: 10.3390/jof11020102.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and interstitial disease with an unclear cause, believed to involve genetic, environmental, and molecular factors. Recent research suggested that Pneumocystis jirovecii (PJ) could contribute to disease exacerbations and severity. This article explores how PJ colonization might influence the pathogenesis of IPF. We performed a proteomic analysis to study the profile of control and IPF patients, with/without PJ. We recruited nine participants from the Virgen del Rocio University Hospital (Seville, Spain). iTRAQ and bioinformatics analyses were performed to identify differentially expressed proteins (DEPs), including a functional analysis of DEPs and of the protein-protein interaction networks built using the STRING database. We identified a total of 92 DEPs highlighting the protein vimentin when comparing groups. Functional differences were observed, with the glycolysis pathway highlighted in PJ-colonized IPF patients; as well as the pentose phosphate pathway and miR-133A in non-colonized IPF patients. We found 11 protein complexes, notably the JAK-STAT signaling complex in non-colonized IPF patients. To our knowledge, this is the first study that analyzed PJ colonization's effect on IPF patients. However, further research is needed, especially on the complex interactions with the AKT/GSK-3β/snail pathway that could explain some of our results.

PMID:39997396 | DOI:10.3390/jof11020102

Cells. 2025 Feb 10;14(4):251. doi: 10.3390/cells14040251.

ABSTRACT

Epigenetics regulates gene expression and thus cellular processes that underlie the pathogenesis of chronic lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and idiopathic pulmonary fibrosis (IPF). Environmental factors (e.g., air pollution, smoking, infections, poverty), but also conditions such as gastroesophageal reflux, induce epigenetic changes long before lung disease is diagnosed. Therefore, epigenetic signatures have the potential to serve as biomarkers that can be used to identify younger patients who are at risk for premature loss of lung function or diseases such as IPF. Epigenetic analyses also contribute to a better understanding of chronic lung disease. This can be used directly to improve therapies, as well as for the development of innovative drugs. Here, we highlight the role of epigenetics in the development and progression of chronic lung disease, with a focus on DNA methylation.

PMID:39996724 | DOI:10.3390/cells14040251

Tissue Eng Regen Med. 2025 Feb 25. doi: 10.1007/s13770-025-00705-0. Online ahead of print.

ABSTRACT

BACKGROUND: Autotaxin (ATX), an ENPP2 enzyme, regulates lipid signaling by converting lysophosphatidylcholine to lysophosphatidic acid (LPA). Dysregulation of the ATX/LPA axis promotes inflammation and disease progression. BMP-22, a lipid ATX inhibitor, effectively reduces LPA production. However, its clinical utility is hampered by limitations in solubility and pharmacokinetics. To overcome these limitations, we developed BMP-22-incorporated lipid nanoparticles (LNP-BMP) to improve utility while maintaining ATX inhibition efficacy.

METHODS: LNP-BMP was synthesized by incorporating DOTAP, DOPE, cholesterol, 18:0 PEG2000-PE, and together with BMP-22. The formulation of LNP-BMP was optimized and characterized by testing different molar ratios of BMP-22. The autophagy recovery and anti-inflammatory effects of LNP-BMP via ATX inhibition were evaluated in both macrophage cell line and mouse-derived primary macrophages.

RESULTS: LNP-BMP was shown to retain its functionality as an ATX inhibitor and maintain the physical characteristics upon BMP-22 integration. Synthesized LNP-BMP exerted superior ability to inhibit ATX activity. When applied to M1-induced macrophages, LNP-BMP exhibited substantial anti-inflammatory effects and successfully restored autophagy activity.

CONCLUSION: The results demonstrate that LNP-BMP effectively inhibits ATX, achieving both anti-inflammatory effects and autophagy restoration, highlighting its potential as a standalone immunotherapeutic agent. Furthermore, the capacity to load therapeutic drugs into this formulation offers promising opportunities for further therapeutic strategies.

PMID:39998744 | DOI:10.1007/s13770-025-00705-0

Brief Bioinform. 2024 Nov 22;26(1):bbaf070. doi: 10.1093/bib/bbaf070.

ABSTRACT

With the exponential growth of biomedical literature, leveraging Large Language Models (LLMs) for automated medical knowledge understanding has become increasingly critical for advancing precision medicine. However, current approaches face significant challenges in reliability, verifiability, and scalability when extracting complex biological relationships from scientific literature using LLMs. To overcome the obstacles of LLM development in biomedical literature understating, we propose LORE, a novel unsupervised two-stage reading methodology with LLM that models literature as a knowledge graph of verifiable factual statements and, in turn, as semantic embeddings in Euclidean space. LORE captured essential gene pathogenicity information when applied to PubMed abstracts for large-scale understanding of disease-gene relationships. We demonstrated that modeling a latent pathogenic flow in the semantic embedding with supervision from the ClinVar database led to a 90% mean average precision in identifying relevant genes across 2097 diseases. This work provides a scalable and reproducible approach for leveraging LLMs in biomedical literature analysis, offering new opportunities for researchers to identify therapeutic targets efficiently.

PMID:39998433 | DOI:10.1093/bib/bbaf070

mBio. 2025 Feb 25:e0401524. doi: 10.1128/mbio.04015-24. Online ahead of print.

ABSTRACT

The gastrointestinal (GI) tract is a site of replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and GI symptoms are often reported by patients. SARS-CoV-2 cell entry depends upon heparan sulfate (HS) proteoglycans, which commensal bacteria that bathe the human mucosa are known to modify. To explore human gut HS-modifying bacterial abundances and how their presence may impact SARS-CoV-2 infection, we developed a task-based analysis of proteoglycan degradation on large-scale shotgun metagenomic data. We observed that gut bacteria with high predicted catabolic capacity for HS differ by age and sex, factors associated with coronavirus disease 2019 (COVID-19) severity, and directly by disease severity during/after infection, but do not vary between subjects with COVID-19 comorbidities or by diet. Gut commensal bacterial HS-modifying enzymes reduce spike protein binding and infection of authentic SARS-CoV-2, suggesting that bacterial grooming of the GI mucosa may impact viral susceptibility.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019, can infect the gastrointestinal (GI) tract, and individuals who exhibit GI symptoms often have more severe disease. The GI tract's glycocalyx, a component of the mucosa covering the large intestine, plays a key role in viral entry by binding SARS-CoV-2's spike protein via heparan sulfate (HS). Here, using metabolic task analysis of multiple large microbiome sequencing data sets of the human gut microbiome, we identify a key commensal human intestinal bacteria capable of grooming glycocalyx HS and modulating SARS-CoV-2 infectivity in vitro. Moreover, we engineered the common probiotic Escherichia coli Nissle 1917 (EcN) to effectively block SARS-CoV-2 binding and infection of human cell cultures. Understanding these microbial interactions could lead to better risk assessments and novel therapies targeting viral entry mechanisms.

PMID:39998226 | DOI:10.1128/mbio.04015-24

mBio. 2025 Feb 25:e0399724. doi: 10.1128/mbio.03997-24. Online ahead of print.

ABSTRACT

Campylobacter jejuni is a common foodborne pathogen worldwide that is associated with high rates of morbidity and mortality among infants in low- to middle-income countries (LMICs). Human milk provides infants with an important source of nutrients and contains antimicrobial components for protection against infection. However, recent studies, including our own, have found significantly higher levels of Campylobacter in diarrheal stool samples collected from breastfed infants compared to non-breastfed infants in LMICs. We hypothesized that C. jejuni has unique strategies to resist the antimicrobial properties of human milk. Transcriptional profiling found human milk exposure induces genes associated with ribosomal function, iron acquisition, and amino acid utilization in C. jejuni strains 81-176 and 11168. However, unidentified proteinaceous components of human milk prevent bacterial growth. Evolving both C. jejuni isolates to survive in human milk resulted in mutations in genes encoding the acyl carrier protein (AcpP) and the major outer membrane porin (PorA). Introduction of the PorA/AcpP amino acid changes into the parental backgrounds followed by electron microscopy showed distinct membrane architectures, and the AcpP changes not only significantly improved growth in human milk, but also yielded cells surrounded with outer membrane vesicles. Analyses of the phospholipid and lipooligosaccharide (LOS) compositions suggest an imbalance in acyl chain distributions. For strain 11168, these changes protect both evolved and 11168∆acpPG33R strains from bacteriophage infection and polymyxin killing. Taken together, this study provides insights into how C. jejuni may evolve to resist the bactericidal activity of human milk and flourish in the hostile environment of the gastrointestinal tract.

IMPORTANCE: In this study, we evolved C. jejuni strains which can grow in the presence of human milk and found that cell membrane alterations may be involved in resistance to the antimicrobial properties of human milk. These bacterial membrane changes are predominantly linked to amino acid substitutions within the acyl carrier protein, AcpP, although other bacterial components, including PorA, are likely involved. This study provides some insights into possible strategies for C. jejuni survival and propagation in the gastrointestinal tract of breastfed infants.

PMID:39998218 | DOI:10.1128/mbio.03997-24

Microbiol Resour Announc. 2025 Feb 25:e0113424. doi: 10.1128/mra.01134-24. Online ahead of print.

ABSTRACT

Rhodococcus sp. strain DK17 degrades various alkylbenzenes, including o-xylene, making it a potential biocatalyst for the production of fine chemicals. DK17 carries the degradative genes on linear mega-plasmids. Here, we present the refined DK17 genome and analyze the genetic variations in UV-induced mutants DK176 and DK180.

PMID:39998182 | DOI:10.1128/mra.01134-24