Sci Rep. 2025 Mar 10;15(1):8314. doi: 10.1038/s41598-025-92289-x.

ABSTRACT

Coxsackievirus B3 (CVB3), a member of the Enterovirus genus within the Picornaviridae family, has emerged as a key model for studying viral evolution and pathogenesis. Although traditionally considered obligate lytic viruses, recent research reveals that enteroviruses can also be released non-lytically within extracellular vesicles (EVs). This study explores the impact of mutations at position 63 of the VP3 capsid protein on CVB3 fitness and release mechanisms by substituting asparagine at this position with aromatic, charged, and aliphatic amino acids. We show that mutations at position 63 significantly affect viral release mechanisms and viral spread in cell culture. Specifically, aromatic mutations (N63H, N63Y, N63F, N63W) and the N63D mutation reduce the release of membrane-associated viral particles, while aromatic residues increase viral spread in cell culture and plaque size under specific conditions. These findings suggest that N63 mutations alter protomer interactions, influencing viral release, spread, and plaque formation, providing insights into the molecular mechanisms of CVB3 egress.

PMID:40064995 | DOI:10.1038/s41598-025-92289-x

Eur Geriatr Med. 2025 Mar 10. doi: 10.1007/s41999-025-01180-5. Online ahead of print.

ABSTRACT

PURPOSE: In this prospective external validation study, we examined the performance of the Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead) postoperative delirium (POD) prediction algorithm. SURGE-Ahead is a collaborative project that aims to develop a clinical decision support system that uses predictive models to support geriatric co-management in surgical wards. Delirium is a common complication in older adults after surgery, leading to poor outcomes and increased healthcare costs. Early and accurate prediction of POD is crucial for timely intervention and prevention strategies.

METHODS: The SURGE-Ahead algorithm utilizes a linear support vector machine model with a comprehensive set of 15 clinical and demographic features. In our validation, we analyzed 173 study participants, of which 50 developed POD.

RESULTS: The study found that the SURGE-Ahead POD prediction algorithm yielded state-of-the-art performance, using only preoperative data, with a receiver operating characteristics area under the curve of 0.86. In addition, the SURGE-Ahead algorithm exhibited good calibration as shown by a Brier Score of 0.14. The algorithm is openly available on GitHub, facilitating its implementation and adaptation to different surgical settings.

CONCLUSION: Our findings contribute to the development of reliable POD prediction tools, ultimately supporting the improvement of patient care in hospitalized older adults.

PMID:40064822 | DOI:10.1007/s41999-025-01180-5

Methods Mol Biol. 2025;2912:191-204. doi: 10.1007/978-1-0716-4454-6_17.

ABSTRACT

Full-length viral genome sequencing is key for virus distribution profiling, new virus discovery, and understanding virus populations across different samples. Circular DNA Enrichment Sequencing (CIDER-Seq) allows unbiased enrichment and long-read sequencing of circular DNA viruses. CIDER-Seq produces single-read full-length virus genomes, combining PCR-free enrichment with Single Molecule Real-Time sequencing and de-concatenation algorithm. CIDER-Seq data analysis package, using the DeConcat algorithm, processes PacBio sequencing data into intact circular DNA sequences, generating fully annotated and highly accurate circular DNA virus genome sequences.

PMID:40064783 | DOI:10.1007/978-1-0716-4454-6_17

Respirol Case Rep. 2025 Mar 10;13(3):e70147. doi: 10.1002/rcr2.70147. eCollection 2025 Mar.

ABSTRACT

Dasatinib, a second-generation tyrosine kinase inhibitor used for treating chronic myeloid leukaemia (CML), is associated with rare but significant adverse effects, including pulmonary arterial hypertension. This condition is thought to result from endothelial dysfunction and vascular remodelling linked to Src kinase inhibition. Symptoms such as progressive dyspnoea and fatigue may appear months or years after starting therapy, emphasising the need for long-term vigilance. We present the case of a 55-year-old female with CML who developed severe pre-capillary pulmonary hypertension after prolonged dasatinib use. Diagnosis was confirmed via echocardiography and right heart catheterisation, with other causes excluded. Following dasatinib discontinuation, initiation of targeted PAH therapy, and replacement with imatinib, the patient showed significant clinical and haemodynamic improvement.

PMID:40065794 | PMC:PMC11893177 | DOI:10.1002/rcr2.70147

J Clin Hypertens (Greenwich). 2025 Mar;27(3):e70029. doi: 10.1111/jch.70029.

ABSTRACT

Hypertension exerts a significant global disease burden, adversely affecting the well-being of billions. Alarmingly, drug-related hypertension remains an area that has not been comprehensively investigated. Therefore, this study is designed to utilize the adverse event reports (AERs) from the US Food and Drug Administration's Adverse Event Reporting System (FAERS) to more comprehensively identify drugs that may potentially lead to hypertension. Specifically, a total of 207 233 AERs were extracted from FAERS, spanning the time period from 2004 to 2024. Based on these reports, this study presented the top 40 drugs most frequently reported to be associated with post-administration hypertension in different genders. Furthermore, we employed four disproportionality analysis methods, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayes Geometric Mean (EBGM), to pinpoint the top three drugs with strongest signals in relation to hypertension across different age and gender subgroups. Some drugs, such as rofecoxib, lenvatinib, and celecoxib, were found to appear on both the frequency and signal strength lists. These results contribute to a more comprehensive understanding of the cardiovascular safety profiles of pharmacological agents, suggesting the necessity of blood pressure monitoring following administration.

PMID:40065662 | DOI:10.1111/jch.70029

bioRxiv [Preprint]. 2025 Mar 1:2025.02.27.640588. doi: 10.1101/2025.02.27.640588.

ABSTRACT

Heterozygous loss-of-function (LoF) variants in RFX3, a transcription factor known to play key roles in ciliogenesis, result in autism spectrum disorder (ASD) and neurodevelopmental delay. RFX binding motifs are also enriched upstream of genes found to be commonly dysregulated in transcriptomic analyses of brain tissue from individuals with idiopathic ASD. Still, the precise functions of RFX3 in the human brain is unknown. Here, we studied the impact of RFX3 deficiency using human iPSC-derived neurons and forebrain organoids. Biallelic loss of RFX3 disrupted ciliary gene expression and delayed neuronal differentiation, while monoallelic loss of RFX3 did not. Instead, transcriptomic and DNA binding analyses demonstrated that monoallelic RFX3 loss disrupted synaptic target gene expression and diminished neuronal activity-dependent gene expression. RFX3 binding sites co-localized with CREB binding sites near activity-dependent genes, and RFX3 deficiency led to decreased CREB binding and impaired induction of CREB targets in response to neuronal depolarization. This study demonstrates a novel role of the ASD-associated gene RFX3 in shaping neuronal synaptic development and plasticity.

PMID:40060598 | PMC:PMC11888390 | DOI:10.1101/2025.02.27.640588

Eur J Pharm Sci. 2025 Mar 8:107063. doi: 10.1016/j.ejps.2025.107063. Online ahead of print.

ABSTRACT

Future long-duration crewed space missions beyond Low Earth Orbit (LEO) will bring new healthcare challenges for astronauts for which pharmacological countermeasures (pharmacological countermeasures) are crucial. This paper highlights current pharmacological countermeasures challenges described in the ESA SciSpacE Roadmap, with a focus on the cardiovascular system as a model to demonstrate the potential implication of the challenges and recommendations. New pharmacological approaches and procedures need to be adapted to spaceflight (spaceflight) conditions, including ethical and reglementary considerations. Potential strategies include combining pharmacological biomarkers such as pharmacogenomics with therapeutic drug monitoring, advancing microsampling techniques, and implementing a pharmacovigilance system to gain deep insights into pharmacokinetics/pharmacodynamics (PK/PD) spaceflight alteration on drug exposure. Emerging therapeutic approaches (such as long-term regimens) or manufacturing drugs in the space environment, can address specific issues related to drug storage and stability. The integration of biobanks and innovative technologies like organoids and organ-on-a-chip, artificial intelligence (AI), including machine learning will further enhance PK modelling leading to personalized treatments. These innovative pharmaceutical tools will also enable reciprocal game-changing healthcare developments to be made on Earth as well as in space and are essential to ensure space explorers receive safe effective pharmaceutical care.

PMID:40064402 | DOI:10.1016/j.ejps.2025.107063

Crit Rev Oncol Hematol. 2025 Mar 8:104694. doi: 10.1016/j.critrevonc.2025.104694. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heterogeneous and the response to therapy varies significantly, limiting personalized treatment options. Certain molecular biomarkers, including microsatellite instability (MSI), are critical in planning personalized treatment, although only a subset of patients may benefit. Currently, the primary methods for assessing MSI status include immunohistochemistry (IHC) for DNA mismatch repair proteins (MMRs), polymerase chain reaction (PCR)-based molecular testing, or next-generation sequencing (NGS). However, these techniques have limitations, are expensive and time-consuming, and often result in inter-method inconsistencies. Deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H) are critical predictive biomarkers of response to immune checkpoint inhibitor (ICI) therapy and MSI testing is recommended to identify patients who may benefit. There is a pressing need for a more robust, reliable, and cost-effective approach that accurately assesses MSI status. Recent advances in computational pathology, in particular the development of technologies that digitally scan whole slide images (WSI) at high resolution, as well as new approaches to artificial intelligence (AI) in medicine, are increasingly gaining ground. This review aims to provide an overview of the latest findings on WSI and advances in AI methods for predicting MSI status, summarize their applications in CRC, and discuss their strengths and limitations in daily clinical practice.

PMID:40064251 | DOI:10.1016/j.critrevonc.2025.104694

J Dent. 2025 Mar 8:105668. doi: 10.1016/j.jdent.2025.105668. Online ahead of print.

ABSTRACT

OBJECTIVES: Convolutional Neural Networks (CNNs) have long dominated image analysis in dentistry, reaching remarkable results in a range of different tasks. However, Transformer-based architectures, originally proposed for Natural Language Processing, are also promising for dental image analysis. The present study aimed to compare CNNs with Transformers for different image analysis tasks in dentistry.

METHODS: Two CNNs (U-Net, DeepLabV3+), two Hybrids (SwinUNETR, UNETR) and two Transformer-based architectures (TransDeepLab, SwinUnet) were compared on three dental segmentation tasks on different image modalities. Datasets consisted of (1) 1881 panoramic radiographs used for tooth segmentation, (2) 1625 bitewings used for tooth structure segmentation, and (3) 2689 bitewings for caries lesions segmentation. All models were trained and evaluated using 5-fold cross-validation.

RESULTS: CNNs were found to be significantly superior over Hybrids and Transformer-based architectures for all three tasks. (1) Tooth segmentation showed mean±SD F1-Score of 0.89±0.009 for CNNs, 0.86±0.015 for Hybrids and 0.83±0.22 for Transformer-based architectures. (2) In tooth structure segmentation CNNs also outperformed with 0.85±0.008 compared to Hybrids 0.84±0.005 and Transformers 0.83±0.011. (3) Even more pronounced results were found for caries lesions segmentation; 0.49±0.031 for CNNs, 0.39±0.072 for Hybrids and 0.32±0.039 for Transformer-based architectures.

CONCLUSION: CNNs significantly outperformed Transformer-based architectures and their Hybrids on three segmentation tasks (teeth, tooth structures, caries lesions) on varying dental data modalities (panoramic and bitewing radiographs).

PMID:40064460 | DOI:10.1016/j.jdent.2025.105668

Photodiagnosis Photodyn Ther. 2025 Mar 8:104552. doi: 10.1016/j.pdpdt.2025.104552. Online ahead of print.

ABSTRACT

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide, necessitating early detection and accurate diagnosis. This study proposes a novel framework integrating Generative Adversarial Networks (GANs) for data augmentation, denoising autoencoders for noise reduction, and transfer learning with EfficientNetB0 to enhance the performance of DR classification models.

METHODS: GANs were employed to generate high-quality synthetic retinal images, effectively addressing class imbalance and enriching the training dataset. Denoising autoencoders further improved image quality by reducing noise and eliminating common artifacts such as speckle noise, motion blur, and illumination inconsistencies, providing clean and consistent inputs for the classification model. EfficientNetB0 was fine-tuned on the augmented and denoised dataset.

RESULTS: The framework achieved exceptional classification metrics, including 99.00% accuracy, recall, and specificity, surpassing state-of-the-art methods. The study employed a custom-curated OCT dataset featuring high-resolution and clinically relevant images, addressing challenges such as limited annotated data and noisy inputs.

CONCLUSIONS: Unlike existing studies, our work uniquely integrates GANs, autoencoders, and EfficientNetB0, demonstrating the robustness, scalability, and clinical potential of the proposed framework. Future directions include integrating interpretability tools to enhance clinical adoption and exploring additional imaging modalities to further improve generalizability. This study highlights the transformative potential of deep learning in addressing critical challenges in diabetic retinopathy diagnosis.

PMID:40064432 | DOI:10.1016/j.pdpdt.2025.104552

Am J Ophthalmol. 2025 Mar 8:S0002-9394(25)00119-9. doi: 10.1016/j.ajo.2025.03.007. Online ahead of print.

ABSTRACT

OBJECTIVE: To identify genetic determinants specific to reticular pseudodrusen (RPD) compared with drusen.

DESIGN: Genome-wide association study (GWAS) SUBJECTS: Participants with RPD, drusen, and controls from the UK Biobank (UKBB), a large, multisite, community-based cohort.

METHODS: A deep learning framework analyzed 169,370 optical coherence tomography (OCT) volumes to identify cases and controls within the UKBB. Five retina specialists validated the cohorts using OCT and color fundus photographs. Several GWAS were undertaken utilizing the quantity and presence of RPD and drusen. Genome-wide significance was defined as p<5e-8.

MAIN OUTCOMES MEASURES: Genetic associations were examined with the number of RPD and drusen within 'pure' cases, where only RPD or drusen were present in either eye. A candidate approach assessed 46 previously known AMD loci. Secondary GWAS were conducted for number of RPD and drusen in mixed cases, and binary case-control analyses for pure RPD and pure drusen.

RESULTS: The study included 1,787 participants: 1,037 controls, 361 pure drusen, 66 pure RPD, and 323 mixed cases. The primary pure RPD GWAS identified four genome-wide significant loci: rs11200630 near ARMS2-HTRA1 (p=1.9e-09), rs79641866 at PARD3B (p=1.3e-08), rs143184903 near ITPR1 (p=8.1e-09), and rs76377757 near SLN (p=4.3e-08). The latter three are uncommon variants (minor allele frequency <5%). A significant association at the CFH locus was also observed using a candidate approach (p=1.8e-04). For pure drusen, two loci reached genome-wide significance: rs10801555 at CFH (p=6.0e-33) and rs61871744 at ARMS2-HTRA1 (p=4.2e-20).

CONCLUSIONS: The study highlights a clear association between the ARMS2-HTRA1 locus and higher RPD load. Although the CFH locus association did not achieve genome-wide significance, a suggestive link was observed. Three novel associations unique to RPD were identified, albeit for uncommon genetic variants. Further studies with larger sample sizes are needed to explore these findings.

PMID:40064387 | DOI:10.1016/j.ajo.2025.03.007

Int J Cardiol. 2025 Mar 8:133140. doi: 10.1016/j.ijcard.2025.133140. Online ahead of print.

ABSTRACT

BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.

METHODS: A total of 100 patients who underwent invasive coronary angiography, OCT, and CCTA before discharge were included in this study. The data were randomly divided into a training set (80 %) and a test set (20 %). The training set, comprising 21,471 tomography images, was used to train a deep-learning convolutional neural network. Subsequently, the AI model was integrated with flow reserve score calculation software developed by Ruixin Medical.

RESULTS: The results from the test set demonstrated excellent agreement between the AI model and OCT analysis for calcified plaque (McNemar test, p = 0.683), non-calcified plaque (McNemar test, p = 0.752), mixed plaque (McNemar test, p = 1.000), and low-attenuation plaque (McNemar test, p = 1.000). Additionally, there was excellent agreement for deep learning-derived minimum lumen diameter (intraclass correlation coefficient [ICC] 0.91, p < 0.001), mean vessel diameter (ICC 0.88, p < 0.001), and percent diameter stenosis (ICC 0.82, p < 0.001). In diagnosing >50 % coronary stenosis, the diagnostic accuracy of the AI model surpassed that of conventional CCTA (AUC 0.98 vs. 0.76, p = 0.008). When compared with quantitative flow fraction, there was excellent agreement between QFR and AI-derived CT-FFR (ICC 0.745, p < 0.0001).

CONCLUSION: Our AI model effectively provides automated analysis of plaque characteristics from CCTA images, with the analysis results showing strong agreement with OCT findings. Moreover, the CT-FFR automatically analyzed by the AI model exhibits high consistency with QFR derived from coronary angiography.

PMID:40064207 | DOI:10.1016/j.ijcard.2025.133140

Comput Biol Med. 2025 Mar 9;189:109941. doi: 10.1016/j.compbiomed.2025.109941. Online ahead of print.

ABSTRACT

OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.

METHODS: An ordinal regression Deep Learning (DL) model was proposed to predict 10-year CVD risk scores. The mechanism of the DL model in understanding CVD risk was explored using methods such as transfer learning and saliency maps.

RESULTS: Model development was performed using data from 34,652 participants with good-quality fundus photographs from the UK Biobank and a dataset for external validation collected in Australia comprised of 1376 fundus photos of 401 participants with a desktop retinal camera and a portable retinal camera. The mean [SD] risk-level accuracies across cross-validation folds was 0.772 [0.008], while AUROC for over moderate risk was 0.849 [0.005] and the AUROC for high risk was 0.874 [0.007] on the UK Biobank dataset. The risk-level accuracy for images acquired with the desktop camera data was 0.715, and the accuracy for portable camera data was 0.656 on the external dataset.

CONCLUSIONS: The DL model described in this study has minimized the underestimation problem. Our analysis confirms that the DL model learned CVD risk score prediction primarily from age- and sex-related image representation. Model performance was only slightly degraded when features such as the retinal vessels and colours were removed from the images. Our analysis identified some image features associated with high CVD risk status, such as the peripheral small vessels and the macula areas.

PMID:40064120 | DOI:10.1016/j.compbiomed.2025.109941

J Neural Eng. 2025 Mar 10. doi: 10.1088/1741-2552/adbebe. Online ahead of print.

ABSTRACT

Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, the presence of various artifacts leads to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The proposed work focuses on the Electromyography (EMG) artifacts, which are among the most challenging biological artifacts. The currently reported EMG artifact cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The data are typically gathered either by recruiting subjects to perform specific EMG artifact tasks or by integrating existing datasets. Prevailing approaches, however, tend to rely on intuitive, concept-oriented data collection with minimal justification for the selection of artifacts and their quantities. Given the substantial costs associated with biological data collection and the pressing need for effective data utilization, we propose an optimization procedure for data-oriented data collection design using deep learning-based artifact detection.Approach. We apply a binary classification differentiating between artifact epochs (time intervals containing EMG artifacts) and non-artifact epochs (time intervals containing no EMG artifact) using three different neural architectures. Our aim is to minimize data collection efforts while preserving the cleaning efficiency.Main results. We were able to reduce the number of EMG artifact tasks from twelve to three and decrease repetitions of isometric&#xD;contraction tasks from ten to three or sometimes even just one.Significance. Our work addresses the need for effective data utilization in biological data collection, offering a systematic and dynamic quantitative approach. By providing clear justifications for the choices of artifacts and their quantity, we aim to guide future studies toward more effective and economical data collection in EEG and EMG research.

PMID:40064096 | DOI:10.1088/1741-2552/adbebe

Int J Biol Macromol. 2025 Mar 8:141890. doi: 10.1016/j.ijbiomac.2025.141890. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by myofibroblasts accumulation and uncontrolled extracellular matrix (ECM) deposition. Here, we reported that activating transcription factor 4 (ATF4), a multifunctional transcription regulatory protein, is overexpressed in IPF lungs and mouse fibrotic lungs, mainly in myofibroblasts and macrophages. Haplodeletion of Atf4 in mice or blockage of Atf4 with Atf4 shRNA-loaded lentiviruses in mice reduced bleomycin (BLM)-induced pulmonary fibrosis (PF) in vivo. Mechanistically, we found that ATF4 directly binds to the promoter of Acta2 (encodes α-SMA), and promotes lung fibroblasts activation and myofibroblasts accumulation. Additionally, ATF4 regulates macrophage M2 program, and promotes TGFβ1 secretion by directly influencing Tgfb1 gene expression in macrophages, subsequently enhances crosstalk between macrophages and lung fibroblasts. These data suggest that strategies for inhibiting ATF4 may represent an effective treatment for PF.

PMID:40064253 | DOI:10.1016/j.ijbiomac.2025.141890

J Prev Alzheimers Dis. 2025 Mar 9:100129. doi: 10.1016/j.tjpad.2025.100129. Online ahead of print.

ABSTRACT

BACKGROUND: Type 2 diabetes (T2D) is commonly co-morbid with Alzheimer's disease (AD). However, it remains unclear whether T2D itself or the antidiabetic drug metformin contributes to the progression of AD.

OBJECTIVE: This study aimed to investigate the overall and independent effects of T2D and metformin use on the risk of AD.

METHODS: Summary genome-wide association study datasets were utilized for the Mendelian randomization (MR) and multivariable MR (MVMR) analyses, including ones for T2D (N = 455,017), metformin (N = 456,276), and AD (N = 453,733). Additionally, using the proportional imbalance method, we analyzed AD-related adverse drug events in the FDA Adverse Event Reporting System (FAERS) database (covering Q1 2004 to Q2 2024).

RESULTS: Our two-sample MR analysis indicated that T2D is not associated with the risk of AD (OR: 1.03, CI: 0.99-1.08, P = 0.128). However, while not statistically significant, genetic signature for metformin exposure demonstrated a trend toward an increased risk of AD (OR: 1.05, CI: 1.00-1.09, P = 0.053). Interestingly, in MVMR analysis, which evaluates independent effects of T2D and metformin exposure on T2D, we found a robust association of T2D with a decrease in the risk of AD (OR: 0.82, CI: 0.68-0.98, P = 0.031), while the use of metformin was associated with a higher risk of AD (OR: 1.26, CI: 1.06-1.50, P = 9.45E-3). In the FAERS database, a total of 228,283 metformin-related adverse event reports from 67,742 cases were found. For metformin as the target drug and AD as the target adverse event, signal analysis reported 29 cases of AD (ROR: 0.83, 95 % CI: 0.58-1.19, P = 0.3126).

CONCLUSIONS: Our study reveals the opposite independent causal effects of T2D and metformin exposure on AD. These findings highlight the importance of assessing AD risk when prescribing metformin to patients with T2D.

PMID:40064559 | DOI:10.1016/j.tjpad.2025.100129

PLoS One. 2025 Mar 10;20(3):e0309191. doi: 10.1371/journal.pone.0309191. eCollection 2025.

ABSTRACT

BACKGROUND: Routine screening for nontuberculous mycobacterial (NTM) lung disease is dependent on sputum cultures. This is particularly challenging in the cystic fibrosis (CF) population due to reduced sputum production and low culture sensitivity. Biomarkers of infection that do not rely on sputum may lead to earlier diagnosis, but validation trials require a unique prospective design.

PURPOSE: The rationale of this trial is to investigate the utility of urine lipoarabinomannan (LAM) as a test to identify people with CF with a new positive NTM culture. We hypothesize that urine LAM is a sensitive, non-invasive screening test with a high negative predictive value to identify individuals with a relatively low risk of having positive NTM sputum culture.

STUDY DESIGN: This is a prospective, single-center, non-randomized observational study in adults with CF, 3 years of negative NTM cultures, and no known history of NTM positive cultures. Patients are followed for two year-long observational periods with the primary endpoint being a positive NTM sputum culture within a year of a positive urine LAM result and a secondary endpoint of a positive NTM sputum culture within 3 years of a positive urine LAM result. Study implementation includes remote consent and sample collection to accommodate changes from the COVID-19 pandemic.

CONCLUSIONS: This report describes the study design of an observational study aimed at using a urine biomarker to assist in the diagnosis of NTM lung infection in pwCF. If successful, urine LAM could be used as an adjunct to traditional sputum cultures for routine NTM screening, and replace cultures in low-risk individuals unable to produce sputum.

PMID:40063876 | DOI:10.1371/journal.pone.0309191

Proc Natl Acad Sci U S A. 2025 Mar 18;122(11):e2424470122. doi: 10.1073/pnas.2424470122. Epub 2025 Mar 10.

ABSTRACT

The cystic fibrosis transmembrane conductance regulator (CFTR) is a chloride channel whose dysfunction leads to intracellular accumulation of chloride ions, dehydration of cell surfaces, and subsequent damage to airway and ductal organs. Beyond its function as a chloride channel, interactions between CFTR, epithelium sodium channel, and solute carrier (SLC) transporter family membrane proteins and cytoplasmic proteins, including calmodulin and Na+/H+ exchanger regulatory factor-1 (NHERF-1), coregulate ion homeostasis. CFTR has also been observed to form mesoscale membrane clusters. However, the contributions of multivalent protein and lipid interactions to cluster formation are not well understood. Using a combination of computational modeling and biochemical reconstitution assays, we demonstrate that multivalent interactions with CFTR protein binding partners, calcium, and membrane cholesterol can induce mesoscale CFTR cluster formation on model membranes. Phosphorylation of the intracellular domains of CFTR also promotes mesoscale cluster formation in the absence of calcium, indicating that multiple mechanisms can contribute to CFTR cluster formation. Our findings reveal that coupling of multivalent protein and lipid interactions promotes CFTR cluster formation consistent with membrane-associated biological phase separation.

PMID:40063811 | DOI:10.1073/pnas.2424470122

J Am Acad Orthop Surg. 2025 Mar 11. doi: 10.5435/JAAOS-D-24-01057. Online ahead of print.

ABSTRACT

Implantation of metallic instrumentation is the mainstay of a variety of orthopaedic and spine surgeries. Postoperatively, imaging of the soft tissues around these implants is commonly required to assess for persistent, recurrent, and/or new pathology (ie, instrumentation loosening, particle disease, infection, neural compression); visualization of these pathologies often requires the superior soft-tissue contrast of magnetic resonance imaging (MRI). As susceptibility artifacts from ferromagnetic implants can result in unacceptable image quality, unique MRI approaches are often necessary to provide accurate imaging. In this text, a comprehensive review is provided on common artifacts encountered in orthopaedic MRI, including comparisons of artifacts from different metallic alloys and common nonpropriety/propriety MR metallic artifact reduction methods. The newest metal-artifact suppression imaging technology and future directions (ie, deep learning/artificial intelligence) in this important field will be considered.

PMID:40063737 | DOI:10.5435/JAAOS-D-24-01057

PLoS One. 2025 Mar 10;20(3):e0317306. doi: 10.1371/journal.pone.0317306. eCollection 2025.

ABSTRACT

Underwater vision is essential in numerous applications, such as marine resource surveying, autonomous navigation, objective detection, and target monitoring. However, raw underwater images often suffer from significant color deviations due to light attenuation, presenting challenges for practical use. This systematic literature review examines the latest advancements in color correction methods for underwater image enhancement. The core objectives of the review are to identify and critically analyze existing approaches, highlighting their strengths, limitations, and areas for future research. A comprehensive search across eight scholarly databases resulted in the identification of 67 relevant studies published between 2010 and 2024. These studies introduce 13 distinct methods for enhancing underwater images, which can be categorized into three groups: physical models, non-physical models, and deep learning-based methods. Physical model-based methods aim to reverse the effects of underwater image degradation by simulating the physical processes of light attenuation and scattering. In contrast, non-physical model-based methods focus on manipulating pixel values without modeling these underlying degradation processes. Deep learning-based methods, by leveraging data-driven approaches, aim to learn mappings between degraded and enhanced images through large datasets. However, challenges persist across all categories, including algorithmic limitations, data dependency, computational complexity, and performance variability across diverse underwater environments. This review consolidates the current knowledge, providing a taxonomy of methods while identifying critical research gaps. It emphasizes the need to improve adaptability across diverse underwater conditions and reduce computational complexity for real-time applications. The review findings serve as a guide for future research to overcome these challenges and advance the field of underwater image enhancement.

PMID:40063649 | DOI:10.1371/journal.pone.0317306