J Physiol Biochem. 2025 Apr 16. doi: 10.1007/s13105-025-01080-z. Online ahead of print.

ABSTRACT

Cardiovascular diseases (CVDs) constitute a major global health problem, being the leading cause of death. Several risk factors for CVDs have been identified, including tobacco use, unhealthy diet, and physical inactivity. However, the role of genetic factors in CVDs remains unclear. Recent studies suggest that vitamin D deficiency is associated with an increased risk of CVDs. Therefore, the aim of this study is to assess the impact of 13 single nucleotide polymorphisms (SNPs) located in genes involved in vitamin D metabolism (VDR, GC, CYP27B1, CYP2R1, and CYP24A1) on the risk of developing CVDs. A retrospective case-control study was conducted in 766 Caucasian individuals from southern Spain: 383 diagnosed with CVDs and 383 without cardiovascular complications, matched based on age and sex. The 13 SNPs were identified by real-time PCR using TaqMan™ probes at the Virgen de las Nieves University Hospital and the University of Granada. According to statistical analysis the allele G and genotype GG of the SNP CYP2R1 rs10741657 and the allele C and CC genotype of the SNP CYP27B1 rs3782130 are associated with a decreased risk of CVDs and diabetes in three of the five heritage models studied. Thus, it can be concluded that CYP2R1 rs10741657 and CYP27B1 rs3782130 could be used as risk biomarkers for CVDs in the future, although studies with a larger number of participants are needed.

PMID:40237935 | DOI:10.1007/s13105-025-01080-z

Clin Infect Dis. 2025 Apr 16:ciaf198. doi: 10.1093/cid/ciaf198. Online ahead of print.

ABSTRACT

BACKGROUND: TB APPRISE (IMPAACT P1078), a Phase IV randomized, multi-country non-inferiority trial assessing the safety of 28 weeks of isoniazid preventive therapy (IPT) initiated during pregnancy (immediate IPT) versus deferring to week 12 postpartum (deferred IPT) in people living with HIV on antiretroviral therapy, showed higher than expected hepatotoxicity. We investigated the potential roles of antiretrovirals, isoniazid, pharmacogenetics and other factors.

METHODS: Hepatotoxicity was defined as Grade≥3 liver enzyme elevations; or Grade≥2 enzyme elevations with elevated bilirubin or symptomatic hepatitis. We performed Poisson regression of all-cause hepatotoxicity on study arm, antiretroviral regimen, pharmacogenetics of isoniazid and efavirenz metabolism (NAT2, CYP2B6) and other participant characteristics. Adjusted models included study arm and covariates with p<0.25 in unadjusted models. Antiretroviral regimen and pharmacogenetics interactions with study arm were evaluated.

RESULTS: All 945 pregnant participants with follow-up liver function measurements were on antiretrovirals (85% with efavirenz, 13% with nevirapine); 63 (6%) experienced hepatotoxicity events; 29 (6%) in immediate and 34 (7%) in deferred arm; only 5 events (8%) occurred in pregnancy; 49 (78%) occurred between delivery and 24 weeks postpartum. Higher risk of hepatotoxicity was observed with nevirapine use in the immediate arm, but there was no difference by study arm in participants on efavirenz. Slow efavirenz metabolizers had increased risk of hepatotoxicity.

CONCLUSIONS: It is critical to monitor for hepatotoxicity in early postpartum, where there is higher risk compared to antepartum. ARV regimen and pharmacogenetics should also be considered in making decisions on when to initiate IPT in pregnant and postpartum populations.

PMID:40237651 | DOI:10.1093/cid/ciaf198

Pediatr Allergy Immunol. 2025 Apr;36(4):e70090. doi: 10.1111/pai.70090.

ABSTRACT

BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is a hypersensitivity response to the allergens of Aspergillus fumigatus, which is the most frequently isolated fungus from the sputum of cystic fibrosis (CF) patients. Because a low number of Th17 lymphocytes is associated with the risk of fungal infections, we investigated inflammatory markers, Th17 cells, and T-cell polarization in CF patients with ABPA.

METHODS: We analyzed the levels of inflammatory markers, blood counts, chemokines, cytokines, and T cell subsets in blood and sputum of CF subjects to elucidate the immunological factors associated with CF patients with Aspergillus fumigatus (AF) positive sputum (AFS+) or with ABPA.

RESULTS: We observed that AFS+ patients have higher sputum and blood IL-6 levels than AF-negative sputum (AFS-) patients. Analysis of blood memory T-helper subsets associated with Th1, Th2, and Th17 polarization among circulating CD45RA-/CD4+ memory T-cell subsets showed higher numbers of CCR4+/CCR6+/CXCR3- and CCR4+/CCR6+/CXCR3+ memory CD4 cells in AFS+ compared to AFS- subjects. Further analysis of Th17-related subsets and IL-17 secreting T cells in subjects with AFS+ showed that those with ABPA have statistically significantly lower levels of Th17 cells as compared to those without ABPA.

CONCLUSION: In CF, AF airway colonization is associated with increased blood counts of Th17-related subsets. However, CF patients with ABPA exhibit lower numbers of CCR4+/CCR6+/CXCR3+ memory CD4 cells and IL-17-secreting CD4 cells compared to control subjects and CF patients without AF sensitization.

PMID:40238087 | DOI:10.1111/pai.70090

Virulence. 2025 Apr 16:2493221. doi: 10.1080/21505594.2025.2493221. Online ahead of print.

ABSTRACT

The incidence of infection by nontuberculous mycobacteria, mainly Mycobacterium abscessus, is increasing in patients with cystic fibrosis and other chronic pulmonary diseases, leading to an accelerated lung function decline. In most cases, M. abscessus coinfects Pseudomonas aeruginosa, the most common pathogen in these conditions. However, how these two bacterial species interact during infection remains poorly understood. This study explored their behaviour in three relevant pathogenic settings: dual-species biofilm development using a recently developed method to monitor individual species in dual-species biofilms, coinfection in bronchial epithelial cells, and in vivo coinfection in the Galleria mellonella model. The results demonstrated that both species form stable mixed biofilms and reciprocally inhibit single-biofilm progression. Coinfections in bronchial epithelial cells significantly decreased cell viability, whereas in G. mellonella, coinfections induced lower survival rates than individual infections. Analysis of the immune response triggered by each bacterium in bronchial epithelial cell assays and G. mellonella larvae revealed that P. aeruginosa induces the overexpression of proinflammatory and melanization cascade responses, respectively. In contrast, M. abscessus and P. aeruginosa coinfection significantly inhibited the immune response in both models, resulting in worse consequences for the host than those generated by a single P. aeruginosa infection. Overall, this study highlights the novel role of M. abscessus in suppressing immune responses during coinfection with P. aeruginosa, emphasizing the clinical implications for the management of cystic fibrosis and other pulmonary diseases. Understanding these interactions could inform the development of new therapeutic strategies to mitigate the severity of coinfections in vulnerable patients.

PMID:40237819 | DOI:10.1080/21505594.2025.2493221

Dentomaxillofac Radiol. 2025 Apr 16:twaf031. doi: 10.1093/dmfr/twaf031. Online ahead of print.

ABSTRACT

OBJECTIVE: This study explores the application of artificial intelligence (AI), specifically deep learning, in the detection and classification of mandibular fractures using CT scans.

MATERIALS AND METHODS: Data from 459 patients were retrospectively obtained from West China Hospital of Stomatology, Sichuan University, spanning from 2020 to 2023. The CT scans were divided into training, testing, and independent validation sets. This research focuses on training and validating a deep learning model using the nnU-Net segmentation framework for pixel-level accuracy in identifying fracture locations. Additionally, a 3D-ResNet with pre-trained weights was employed to classify fractures into three types based on severity. Performance metrics included sensitivity, precision, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS: The study achieved high diagnostic accuracy in mandibule fracture detection, with sensitivity>0.93, precision>0.79, and specificity>0.80. For mandibular fracture classification, accuracies were all above 0.718, with a mean AUC of 0.86.

CONCLUSION: Detection and classification of mandibular fractures in CT images can be significantly enhanced using the nnU-Net segmentation framework, aiding in clinical diagnosis.

PMID:40238181 | DOI:10.1093/dmfr/twaf031

JAMA Cardiol. 2025 Apr 16. doi: 10.1001/jamacardio.2025.0498. Online ahead of print.

ABSTRACT

IMPORTANCE: Accurate assessment of tricuspid regurgitation (TR) is necessary for identification and risk stratification.

OBJECTIVE: To design a deep learning computer vision workflow for identifying color Doppler echocardiogram videos and characterizing TR severity.

DESIGN, SETTING, AND PARTICIPANTS: An automated deep learning workflow was developed using 47 312 studies (2 079 898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. Data analysis was performed in 2024. The pipeline was tested on a temporally distinct test set of 2462 studies (108 138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5549 studies (278 377 videos) from Stanford Healthcare (SHC). Training and validation cohorts contained data from 31 708 patients at CSMC receiving care between 2011 and 2021. Patients were chosen for parity across TR severity classes, with no exclusion criteria based on other clinical or demographic characteristics. The 2022 CSMC test cohort and SHC test cohorts contained studies from 2170 patients and 5014 patients, respectively.

EXPOSURE: Deep learning computer vision model.

MAIN OUTCOMES AND MEASURES: The main outcomes were area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in identifying apical 4-chamber (A4C) videos with color Doppler across the tricuspid valve and AUC in identifying studies with moderate to severe or severe TR.

RESULTS: In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (95% CI, 0.999-1.000) and identified at least 1 A4C video with color Doppler across the tricuspid valve in 2410 of 2462 studies with a sensitivity of 0.975 (95% CI, 0.968-0.982) and a specificity of 1.000 (95% CI, 1.000-1.000). In the CSMC test cohort, moderate or severe TR was detected with an AUC of 0.928 (95% CI, 0.913-0.943), and severe TR was detected with an AUC of 0.956 (95% CI, 0.940-0.969). In the SHC cohort, the view classifier correctly identified at least 1 TR color Doppler video in 5268 of the 5549 studies, resulting in an AUC of 0.999 (95% CI, 0.998-0.999), a sensitivity of 0.949 (95% CI, 0.944-0.955), and a specificity of 0.999 (95% CI, 0.999-0.999). The artificial intelligence model detected moderate or severe TR with an AUC of 0.951 (95% CI, 0.938-0.962) and severe TR with an AUC of 0.980 (95% CI, 0.966-0.988).

CONCLUSIONS AND RELEVANCE: In this study, an automated pipeline was developed to identify clinically significant TR with excellent performance. With open-source code and weights, this project can serve as the foundation for future prospective evaluation of artificial intelligence-assisted workflows in echocardiography.

PMID:40238103 | DOI:10.1001/jamacardio.2025.0498

EJNMMI Phys. 2025 Apr 16;12(1):38. doi: 10.1186/s40658-025-00745-4.

ABSTRACT

BACKGROUND: A reduction of dose and/or acquisition duration of PET examinations is desirable in terms of radiation protection, patient comfort and throughput, but leads to decreased image quality due to poorer image statistics. Recently, different deep-learning based methods have been proposed to improve image quality of low-count PET images. For example, one such approach allows the generation of AI-enhanced PET images (AI-PET) based on ultra-low count PET/CT scans. The performance of this algorithm has so far only been clinically evaluated on patient data featuring limited scan statistics and unknown actual activity concentration. Therefore, this study investigates the performance of this deep-learning algorithm using PET measurements of a phantom resembling different lesion sizes and count statistics (from ultra-low to high) to understand the capabilities and limitations of AI-based post processing for improved image quality in ultra-low count PET imaging.

METHODS: A previously trained pix2pixHD Generative Adversarial Network was evaluated. To this end, a NEMA PET body phantom filled with two sphere-to-background activity concentration ratios (4:1 and 10:1) and two attenuation scenarios to investigate the effects of obese patients was scanned in list mode. Images were reconstructed with 13 different acquisition durations ranging from 5 s up to 900 s. Image noise, recovery coefficients, SUV-differences, image quality measurement metrics such as the Structural Similarity Index Metric, and the contrast-to-noise-ratio were assessed. In addition, the benefits of the deep-learning network over Gaussian smoothing were investigated.

RESULTS: The presented AI-algorithm is very well suitable for denoising ultra-low count PET images and for restoring structural information, but increases image noise in ultra-high count PET scans. The generated AI-PET scans strongly underestimate SUV especially in small lesions with a diameter ≤ 17 mm, while quantitative measures of large lesions ≥ 37 mm in diameter were accurately recovered. In ultra-low count or low contrast images, the AI algorithm might not be able to recognize small lesions ≤ 13 mm in diameter. In comparison to standardized image post-processing using a Gaussian filter, the deep-learning network is better suited to improve image quality, but at the same time degrades SUV accuracy to a greater extent than post-filtering and quantitative SUV accuracy varies for different lesion sizes.

CONCLUSIONS: Phantom-based validation of AI-based algorithms allows for a detailed assessment of the performance, limitations, and generalizability of deep-learning based algorithms for PET image enhancement. Here it was confirmed that the AI-based approach performs very well in denoising ultra-low count PET images and outperforms traditional Gaussian post-filtering. However, there are strong limitations in terms of quantitative accuracy and detectability of small lesions.

PMID:40237913 | DOI:10.1186/s40658-025-00745-4

Discov Oncol. 2025 Apr 16;16(1):537. doi: 10.1007/s12672-025-02329-1.

ABSTRACT

BACKGROUND: Breast cancer (BC) remains a leading cause of cancer-related mortality among women globally, with increasing incidence rates posing significant public health challenges. Recent advancements in artificial intelligence (AI) have revolutionized medical imaging, particularly in enhancing diagnostic accuracy and prognostic capabilities for BC. While multimodal imaging combined with AI has shown remarkable potential, a comprehensive analysis is needed to synthesize current research and identify emerging trends and hotspots in AI-assisted multimodal imaging for BC.

METHODS: This study analyzed literature on AI-assisted multimodal imaging in BC from January 2010 to November 2024 in Web of Science Core Collection (WoSCC). Bibliometric and visualization tools, including VOSviewer, CiteSpace, and the Bibliometrix R package, were employed to assess countries, institutions, authors, journals, and keywords.

RESULTS: A total of 80 publications were included, revealing a steady increase in annual publications and citations, with a notable surge post-2021. China led in productivity and citations, while Germany exhibited the highest citation average. The United States demonstrated the strongest international collaboration. The most productive institution and author are Radboud University Nijmegen and Xi, Xiaoming. Publications were predominantly published in Computerized Medical Imaging and Graphics, with Qian, XJ's 2021 study on BC risk prediction under deep learning frameworks being the most influential. Keyword analysis highlighted themes such as "breast cancer", "classification", and "deep learning".

CONCLUSIONS: AI-assisted multimodal imaging has significantly advanced BC diagnosis and management, with promising future developments. This study offers researchers a comprehensive overview of current frameworks and emerging research directions. Future efforts are expected to focus on improving diagnostic precision and refining therapeutic strategies through optimized imaging techniques and AI algorithms, emphasizing international collaboration to drive innovation and clinical translation.

PMID:40237900 | DOI:10.1007/s12672-025-02329-1

Radiol Artif Intell. 2025 Apr 16:e240097. doi: 10.1148/ryai.240097. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To present and evaluate Dafne (deep anatomic federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of radiologic images through federated incremental learning. Materials and Methods Dafne is free software with a client-server architecture. The client side is an advanced user interface that applies the deep learning models stored on the server to the user's data and allows the user to check and refine the prediction. Incremental learning is then performed at the client's side and sent back to the server, where it is integrated into the root model. Dafne was evaluated locally, by assessing the performance gain across model generations on 38 MRI datasets of the lower legs, and through the analysis of real-world usage statistics (n = 639 use-cases). Results Dafne demonstrated a statistically improvement in the accuracy of semantic segmentation over time (average increase of the Dice Similarity Coefficient by 0.007 points/generation on the local validation set, P < .001). Qualitatively, the models showed enhanced performance on various radiologic image types, including those not present in the initial training sets, indicating good model generalizability. Conclusion Dafne showed improvement in segmentation quality over time, demonstrating potential for learning and generalization. ©RSNA, 2025.

PMID:40237599 | DOI:10.1148/ryai.240097

Radiol Artif Intell. 2025 May;7(3):e250125. doi: 10.1148/ryai.250125.

NO ABSTRACT

PMID:40237597 | DOI:10.1148/ryai.250125

J Agric Food Chem. 2025 Apr 16. doi: 10.1021/acs.jafc.4c11492. Online ahead of print.

ABSTRACT

Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental and sample changes. Hyperspectral imaging technology combined with deep learning methods can effectively overcome these problems. Compared with human evaluation, automated inspection improves inspection efficiency, reduces subjective error, and promotes the intelligent and precise fruit and vegetable quality inspection. This paper reviews reports on the application of hyperspectral imaging technology combined to deep learning methods in various aspects of fruits and vegetables quality assessment. In addition, the latest applications of these technologies in the fields of fruit and vegetable safety, internal quality, and external quality inspection are reviewed, and the challenges and future development directions of hyperspectral imaging technology combined with deep learning in this field are prospected. Hyperspectral imaging combined with deep learning has shown significant advantages in fruit and vegetable quality inspection, especially in improving inspection accuracy and efficiency. Future research should focus on reducing costs, optimizing equipment, personalizing feature extraction, and model generalizability. In addition, the development of lightweight models and the balance of accuracy, the enhancement of the database and the importance of quantitative research should also be brought to attention. These efforts will promote the wide application of hyperspectral imaging technology in fruit and vegetable inspection, improve its practicability in the actual production environment, and bring important progress for food safety and quality management.

PMID:40237548 | DOI:10.1021/acs.jafc.4c11492

mSystems. 2025 Apr 16:e0145524. doi: 10.1128/msystems.01455-24. Online ahead of print.

ABSTRACT

Deep learning is revolutionizing biomedical research by facilitating the integration of multi-omics data sets while bridging classical bioinformatics with existing knowledge. Building on this powerful potential, Zhang et al. proposed a semi-supervised learning framework called Autoencoder-Based Subtypes Detector for Cancer (ASD-cancer) to improve the multi-omics data analysis (H. Zhang, X. Xiong, M. Cheng, et al., 2024, mSystems 9:e01395-24, https://doi.org/10.1128/msystems.01395-24). By utilizing autoencoders pre-trained on The Cancer Genome Atlas data, the ASD-cancer framework outperforms the baseline model. This approach also makes the framework scalable, enabling it to process new data sets through transfer learning without retraining. This commentary explores the methodological innovations and scalability of ASD-cancer while suggesting future directions, such as the incorporation of additional data layers and the development of adaptive AI models through continuous learning. Notably, integrating large language models into ASD-cancer could enhance its interpretability, providing more profound insights into oncological research and increasing its influence in cancer subtyping and further analysis.

PMID:40237527 | DOI:10.1128/msystems.01455-24

Clin Nucl Med. 2025 Apr 16. doi: 10.1097/RLU.0000000000005898. Online ahead of print.

ABSTRACT

PURPOSE: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various deep learning models, including the ConvNeXt and transformer models, in diagnosing metastatic lesions from bone scans.

MATERIALS AND METHODS: We retrospectively analyzed bone scans from patients with cancer obtained at 2 institutions: the training and validation sets (n=4626) were from Hospital 1 and the test set (n=1428) was from Hospital 2. The deep learning models evaluated included ResNet18, the Data-Efficient Image Transformer (DeiT), the Vision Transformer (ViT Large 16), the Swin Transformer (Swin Base), and ConvNeXt Large. Gradient-weighted class activation mapping (Grad-CAM) was used for visualization.

RESULTS: Both the validation set and the test set demonstrated that the ConvNeXt large model (0.969 and 0.885, respectively) exhibited the best performance, followed by the Swin Base model (0.965 and 0.840, respectively), both of which significantly outperformed ResNet (0.892 and 0.725, respectively). Subgroup analyses revealed that all the models demonstrated greater diagnostic accuracy for patients with polymetastasis compared with those with oligometastasis. Grad-CAM visualization revealed that the ConvNeXt Large model focused more on identifying local lesions, whereas the Swin Base model focused on global areas such as the axial skeleton and pelvis.

CONCLUSIONS: Compared with traditional CNN and transformer models, the ConvNeXt model demonstrated superior diagnostic performance in detecting bone metastases from bone scans, especially in cases of polymetastasis, suggesting its potential in medical image analysis.

PMID:40237349 | DOI:10.1097/RLU.0000000000005898

Langmuir. 2025 Apr 16. doi: 10.1021/acs.langmuir.4c04123. Online ahead of print.

ABSTRACT

Molecular motors and the cytoskeleton perform essential cellular functions by interacting with the cell membrane. Reconstituting the behavior of motor proteins interacting with biological membranes and cytoskeletal filaments within a cell-sized space provides insights into their intracellular functions. A water-in-oil (W/O) emulsion droplet represents an invaluable experimental system because it offers an encapsulated space that mimics the intracellular environment. In this study, we aimed to reconstitute the actomyosin motility on the flat membrane surface of cell-sized W/O droplets using membrane-bound myosin I that anchors to and exerts force on both the cell membrane and actin cytoskeleton. Myosin IC or myosin ID, which binds specifically to the cell membrane phospholipid phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] together with the actin cytoskeleton, was encapsulated within W/O droplets. Myosin IC and myosin ID caused gliding of actin filaments on the inner bottom membrane of the hemispherical droplet containing PI(4,5)P2, and myosin-driven actin filament movement on the flat membrane surface was quantified. Fast motor myosin ID was more sensitive than slow motor myosin IC to the geometrical conditions and binding manner to the membrane. Therefore, the in-droplet actin filament gliding assay is a useful tool for elucidating the molecular mechanisms underlying cellular events occurring at the cell membrane, which are achieved through the concerted action of myosin and the actin cytoskeleton.

PMID:40238146 | DOI:10.1021/acs.langmuir.4c04123

Virus Genes. 2025 Apr 16. doi: 10.1007/s11262-025-02157-z. Online ahead of print.

ABSTRACT

Maize streak virus (MSV) has four genes: cp, encoding the coat protein; mp, the movement protein; and repA and rep, encoding two distinct replication-associated proteins from an alternatively spliced transcript. These genes play roles in encapsidation, movement, replication, and interactions with the external environment, making them prone to stimuli-driven molecular adaptation. We accomplished selection studies on publicly available curated, recombination-free, complete coding sequences for representative A-strain maize streak virus (MSV-A) cp and mp genes. We found evidence of gene-wide selection in these two MSV genes at specific sites within the genes (cp 1.23% and mp 0.99%). Positively selected sites have amino acids that are 60% hydrophilic and 40% hydrophobic in nature. We found significant evidence of positive selection at branches (cp: 0.76 and mp:1.66%) representing the diversity of MSV-A-strain in South Africa, which is related to the MSV-A-matA isolate (GenBank accession number: AF329881), well disseminated and adapted to the maize plant in sub-Saharan Africa. In the mp gene, selection significantly intensified for the overall diversities of the MSV-A sequences and those more related to the MSV-Mat-A isolate. These findings reveal that despite predominantly undergoing non-diversifying selection, the detectable diversifying positive selection observed in these genes may play a major role in MSV-A host adaptive evolution, ensuring sufficient pathogenicity for onward transmission without killing the host.

PMID:40237943 | DOI:10.1007/s11262-025-02157-z

J Clin Microbiol. 2025 Apr 16:e0189324. doi: 10.1128/jcm.01893-24. Online ahead of print.

ABSTRACT

This study aimed to compare the performance of disk diffusion, gradient test (ETEST), and VITEK 2 (AST-N223, AST-N428, AST-N432 cards) antibiotic susceptibility testing methods with the reference broth microdilution (BMD) for carbapenem susceptibility in OXA-48-like carbapenemase-producing Enterobacterales (CPE). A total of 107 CPE and 142 controls (Enterobacterales that do not produce any type of carbapenemases), all molecularly characterized by whole-genome sequencing, were tested for carbapenem susceptibility using BMD and derivative methods. Essential agreement (EA), categorical agreement (CA), major error, very major error, and bias were evaluated. In the OXA-48-like group, resistance frequencies by BMD for ertapenem, imipenem, and meropenem were 86.9%, 12.1%, and 10.3%, respectively. For OXA-48-like CPE, ETEST showed the highest EA among all methods for meropenem (100/107, 93.5%) and ertapenem (99/107, 92.5%), while EA for VITEK 2 cards were <90%. In contrast, for imipenem, VITEK 2 AST-N428 performed best with an EA of 95/105 (90.5%). CA was higher for ertapenem across all methods (93.5%-98.1%) compared to imipenem (59.8%-81.4%) and meropenem (78.8%-95.3%). The highest CA was achieved with ETEST for ertapenem and meropenem, and with VITEK 2 AST-N223 for imipenem. Significant variability was observed across different tests in resistance frequencies, MICs, EA, and CA for the OXA-48-like group. Ertapenem was the most useful carbapenem for detecting resistance in OXA-48-like CPE across all methods. Laboratories should be aware that susceptibility testing of imipenem leads to more erroneous results compared to the other carbapenems when using derivative methods. Additionally, most derivative methods tend to overcall carbapenem resistance in OXA-48-like CPE.IMPORTANCEOXA-48-like is the most frequent carbapenemase in western Europe, and both its rapid spread and its challenging-to-detect nature are a particular concern for adequate treatment and infection control purposes. Accurate determination of carbapenem minimal inhibitory concentrations (MICs) is of utmost importance, both for the selection of the best therapy and as a marker for carbapenemase detection. However, the performance of derivative susceptibility testing methods is unclear for OXA-48-like isolates. Our study reports on the varying performance of carbapenem susceptibility testing by disk diffusion, gradient test (ETEST), and VITEK 2 in OXA-48-like-producing Enterobacterales. The results of the present study can help to inform about the limitations of current susceptibility testing methods and serve to improve MIC determination in these challenging isolates.

PMID:40237520 | DOI:10.1128/jcm.01893-24

Genetics. 2025 Apr 16:iyaf073. doi: 10.1093/genetics/iyaf073. Online ahead of print.

ABSTRACT

Heritable fitness differences between individuals are the currency of evolution but can be challenging to quantify with precision. A slight probabilistic fitness advantage to one relatively healthy individual over another is often too subtle to detect in a single generation. For this reason, we have developed an assay to quantify and compare heritable fitness traits in nematodes by allowing their differences to amplify during unrestricted exponential population growth over multiple generations. This method employs continuous imaging as populations expand, and an automated program to detect the time of resource exhaustion. Expanding on our earlier applications, we here describe, motivate, and validate the method's experimental parameters and introduce a new R package to facilitate image processing and statistical analyses. We demonstrate the utility of this method by using it to identify natural differences in mutagen sensitivity between wild isolates of C. elegans. This tool is immediately adaptable to any strain of C. elegans or similar nematode and can be used to quantify fitness differences in the face of any experimental condition that can be created on a petri dish.

PMID:40237334 | DOI:10.1093/genetics/iyaf073

Clin Transl Sci. 2025 Apr;18(4):e70214. doi: 10.1111/cts.70214.

ABSTRACT

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease. Its progressively expanding, fluid-filled renal cysts eventually lead to end-stage renal disease. Despite the relatively high prevalence, treatment options are currently limited to a single drug approved by the FDA and EMA. Here, we investigated human ADPKD patient-derived three-dimensional cyst cultures (3DCC) as an in vitro model for ADPKD and drug repurposing research. First, we analyzed the proteomes of 3DCC derived from healthy and diseased tissues. We then compared the protein expression profiles with those of reference tissues, mainly from the same patients. We quantified 290 proteins affecting drug disposition and proposed target proteins for drug treatment. Lastly, we investigated the functional response of the quantified target proteins after exposure to repurposing candidates in the 3DCC. Proteomic profiling of human 3DCC reflected previously reported pathophysiological alterations, including aberrant protein expression in inflammation and metabolic reprogramming. While the 3DCCs largely recapitulated the disease phenotype in vitro, drug transporter expression was reduced compared to in vivo conditions. Target proteins for proposed repurposing candidates showed similar expression in vitro and in tissues. Exposure to these repurposing candidates inhibited cyst swelling in vitro, supporting the suitability of the 3DCC for ADPKD drug screening. In summary, our results provide new insights into the ADPKD proteome and offer a starting point for further research to improve treatment options for affected individuals.

PMID:40235151 | DOI:10.1111/cts.70214

Health Expect. 2025 Apr;28(2):e70260. doi: 10.1111/hex.70260.

ABSTRACT

BACKGROUND: Research about patient engagement for people with rare diseases has identified how the experiences of some members of the public are overlooked in relation to clinical trial design and trial participation. As part of a knowledge transfer partnership (KTP), the authors were granted access to patient insight reports about the needs of people with idiopathic pulmonary fibrosis (IPF), to inform clinical trial design and marketing strategy. These were contrasted with data from qualitative interviews, informed by and collected from people with IPF and the clinical staff who recruit them to trials.

OBJECTIVE: To identify patient and professional perspectives for IPF drug trials to create opportunities for innovation in patient engagement.

DESIGN: Ethnography. Qualitative researcher embedded in a pharmaceutical organisation.

SETTING AND PARTICIPANTS: International patient insight reports to inform a clinical trial protocol (n = 1) and marketing strategy (n = 6), including the experiences of over 100 patients with IPF. In the United Kingdom, interviews with patients with IPF (n = 32) and the staff who support them clinically and recruit them to trials of new medicines (n = 19) at one specialist interstitial lung disease (ILD) centre.

RESULTS: Methodological practices inherent in inpatient insight reports ensured the perspectives of some people with IPF were overlooked. Interviews with a more marginalised population of people with IPF, and the staff who support them, identified that some found trial information confusing, trial practices frustrating and the opportunities to engage in trial design absent.

DISCUSSION: Current pharmaceutical practices of working with contract research organisations and patient organisations exclude the perspectives of patients with IPF who do not engage with either. Trial recruitment information needs to be tailored to the needs of individuals, and trial processes need to enable a wider group of patients to participate.

CONCLUSIONS: People with IPF want the opportunity to participate in drug trials and trial science. However, methodological rigour and deliberative practices are required to enable a wider group of patients to have a stake in the design and conduct of drug trials for rare diseases. The challenge now is for regulators to mandate such inclusive practices and for pharmaceutical organisations to adopt them.

PATIENT OR PUBLIC CONTRIBUTION: A Patient Advisory Group (PAG) comprising six people with IPF gave input on the research protocol and then on the scope and content of the ongoing research. Two patients from international patient organisations served as a Steering Group (SG). Members of these groups provided their interpretations of the study findings and gave insight on their experiences in clinical design and participation.

PMID:40235185 | DOI:10.1111/hex.70260

ACS Med Chem Lett. 2025 Mar 20;16(4):552-559. doi: 10.1021/acsmedchemlett.4c00592. eCollection 2025 Apr 10.

ABSTRACT

The M2 muscarinic acetylcholine receptor (M2R) is a G protein-coupled receptor involved in regulating cardiovascular functions and mediation of central muscarinic effects, such as movement, temperature control, and antinociceptive responses. Molecular probes targeting this receptor are therefore important in exploring its pathophysiological role at a molecular level. Herein, we report the design, synthesis, and evaluation of a new fluorescent probe for M2R based on an anthranilamide ligand. In radioligand binding experiments, the presented Oregon Green 488-labeled conjugate (33) exhibited high M2R affinity (K i = 2.4 nM), a moderate preference for the M2R over the M4 receptor, and excellent to pronounced M2R selectivity compared to the M1, M3, and M5 receptors. The utility of the probe was demonstrated in confocal, two-photon, and stimulated emission depletion nanoscopy (STED) imaging to specifically label the receptors in human embryonic kidney (HEK) 293T cells. These properties suggest that our probe may be utilized in advanced microscopy to study the pharmacology of the M2R.

PMID:40236555 | PMC:PMC11995211 | DOI:10.1021/acsmedchemlett.4c00592