Front Cell Infect Microbiol. 2025 May 9;15:1569331. doi: 10.3389/fcimb.2025.1569331. eCollection 2025.

ABSTRACT

INTRODUCTION: Chronic bacterial infections are responsible for significant morbidity and mortality in cystic fibrosis (CF) patients. Pseudomonas aeruginosa (Pa), the dominant CF pathogen, and Mycobacterium abscessus (Mab) can individually cause persistent, difficult to treat pulmonary infections. Co-infection by both pathogens leads to severe disease and poor clinical outcomes. Although interactions between Pa and other co-infecting pathogens in CF patients have been the focus of numerous studies, the dynamics of Pa-Mab interactions remain poorly understood.

METHODS: To address this knowledge gap, the study examined how Mab and Pa influenced each other through culture-based growth assays and molecular-based dual RNAseq analysis. Growth was measured by CFU determination and luminescence reporter -based readouts.

RESULTS: In initial studies, we noted that the growth of Pa continued unimpeded in a planktonic co-culture model, whereas Pa appeared to exert a bacteriostatic effect on Mab. Strikingly, exposure of Mab to cell-free spent supernatant of Pa resulted in a dramatic, dose-dependent bactericidal effect. Initial characterization indicated that this potent Pa-derived anti-Mab cidal activity was mediated by a heat-sensitive, protease-insensitive soluble factor of >3kDa size. Further analysis demonstrated that expression of this mycobactericidal factor requires LasR, a central regulator of Pa quorum sensing (QS). In contrast, ΔLasR Pa was still able to exert a bacteriostatic effect on Mab during co-culture, pointing to additional LasR-independent factors able to antagonize Mab growth. However, the ability of Mab to adapt during co-culture to counter the cidal effects of a LasR regulated factor suggested complex interspecies dynamics. Dual RNAseq analysis of Mab-Pa co-cultures revealed significant transcriptional remodeling of Mab, with differential expression of 68% of Mab genes compared to minimal transcriptional changes in Pa. Transcriptome analysis reflected slowed Mab growth and metabolic changes akin to a non-replicating persister phase. A tailored Mab response to Pa was evident by enhanced transcript levels of genes predicted to counteract alkylquinolone QS signals, respiratory toxins, and hydrogen cyanide.

DISCUSSION: The study showed Mab is capable of coexisting with Pa despite Pa's antagonistic effects, eliciting an adaptive molecular response in Mab. This study provides the first transcriptome-level insight into genetic interactions between the two CF pathogens offering potential strategies for disrupting their communities in a CF lung to improve patient clinical performance. Moreover, identification of a novel antimicrobial natural product with potent cidal activity against Mab could lead to new drug targets and therapies for Mab infections.

PMID:40415956 | PMC:PMC12098619 | DOI:10.3389/fcimb.2025.1569331

Front Bioeng Biotechnol. 2025 May 9;13:1550875. doi: 10.3389/fbioe.2025.1550875. eCollection 2025.

ABSTRACT

INTRODUCTION: Bathing is a primary daily activity. Existing bathing systems are limited by their lack of intelligence and adaptability, reliance on caregivers, and the complexity of their control algorithms. Although visual sensors are widely used in intelligent systems, current intelligent bathing systems do not effectively process depth information from these sensors.

METHODS: The scrubbing task of the intelligent bath assist system can be divided into a pre-contact localization phase and a post-contact adaptive scrubbing phase. YOLOv5s, known for its ease of deployment and high accuracy, is utilized for multi-region skin detection to identify different body parts. The depth correction algorithm is designed to improve the depth accuracy of RGB-D vision sensors. The 3D position and pose of the target point in the RGB camera coordinate system are modeled and then transformed to the robot base coordinate system by hand-eye calibration. The system localization accuracy is measured when the collaborative robot runs into contact with the target. The self-rotating end scrubber head has flexible bristles with an adjustable length of 10 mm. After the end is in contact with the target, the point cloud scrubbing trajectory is optimized using cubic B-spline interpolation. Normal vectors are estimated based on approximate triangular dissected dyadic relations. Segmented interpolation is proposed to achieve real-time planning and to address the potential effects of possible unexpected movements of the target. The position and pose updating strategy of the end scrubber head is established.

RESULTS: YOLOv5s enables real-time detection, tolerating variations in skin color, water vapor, occlusion, light, and scene. The localization error is relatively small, with a maximum value of 2.421 mm, a minimum value of 2.081 mm, and an average of 2.186 mm. Sampling the scrubbing curve every 2 mm along the x-axis and comparing actual to desired trajectories, the y-axis shows a maximum deviation of 2.23 mm, which still allows the scrubbing head to conform to the human skin surface.

DISCUSSION: The study does not focus on developing complex control algorithms but instead emphasizes improving the accuracy of depth data to enhance localization precision.

PMID:40416315 | PMC:PMC12098527 | DOI:10.3389/fbioe.2025.1550875

JMA J. 2025 Apr 28;8(2):361-362. doi: 10.31662/jmaj.2025-0076. Epub 2025 Mar 21.

NO ABSTRACT

PMID:40416027 | PMC:PMC12095351 | DOI:10.31662/jmaj.2025-0076

JMA J. 2025 Apr 28;8(2):354-360. doi: 10.31662/jmaj.2024-0312. Epub 2025 Feb 21.

ABSTRACT

The evolution of innovative artificial intelligence (AI) systems in pediatric hospitals in Japan promises benefits for patients and healthcare providers. We actively contribute to advancements in groundbreaking medical treatments by leveraging deep learning technology and using vast medical datasets. Our team of data scientists closely collaborates with departments within the hospital. Our research themes based on deep learning are wide-ranging, including acceleration of pathological diagnosis using image data, distinguishing of bacterial species, early detection of eye diseases, and prediction of genetic disorders from physical features. Furthermore, we implement Information and Communication Technology to diagnose pediatric cancer. Moreover, we predict immune responses based on genomic data and diagnose autism by quantifying behavior and communication. Our expertise extends beyond research to provide comprehensive AI development services, including data collection, annotation, high-speed computing, utilization of machine learning frameworks, design of web services, and containerization. In addition, as active members of medical AI platform collaboration partnerships, we provide unique data and analytical technologies to facilitate the development of AI development platforms. Furthermore, we address the challenges of securing medical data in the cloud to ensure compliance with stringent confidentiality standards. We will discuss AI's advancements in pediatric hospitals and their challenges.

PMID:40415999 | PMC:PMC12095641 | DOI:10.31662/jmaj.2024-0312

JMA J. 2025 Apr 28;8(2):664. doi: 10.31662/jmaj.2024-0420. Epub 2025 Mar 7.

NO ABSTRACT

PMID:40415986 | PMC:PMC12095420 | DOI:10.31662/jmaj.2024-0420

Front Neurosci. 2025 May 9;19:1615276. doi: 10.3389/fnins.2025.1615276. eCollection 2025.

NO ABSTRACT

PMID:40415892 | PMC:PMC12098266 | DOI:10.3389/fnins.2025.1615276

J Med Imaging (Bellingham). 2025 May;12(3):035501. doi: 10.1117/1.JMI.12.3.035501. Epub 2025 May 21.

ABSTRACT

PURPOSE: Breast cancer remains a leading cause of death for women. Screening programs are deployed to detect cancer at early stages. One current barrier identified by breast imaging researchers is a shortage of labeled image datasets. Addressing this problem is crucial to improve early detection models. We present an active learning (AL) framework for segmenting breast masses from 2D digital mammography, and we publish labeled data. Our method aims to reduce the input needed from expert annotators to reach a fully labeled dataset.

APPROACH: We create a dataset of 1136 mammographic masses with pixel-wise binary segmentation labels, with the test subset labeled independently by two different teams. With this dataset, we simulate a human annotator within an AL framework to develop and compare AI-assisted labeling methods, using a discriminator model and a simulated oracle to collect acceptable segmentation labels. A UNet model is retrained on these labels, generating new segmentations. We evaluate various oracle heuristics using the percentage of segmentations that the oracle relabels and measure the quality of the proposed labels by evaluating the intersection over union over a validation dataset.

RESULTS: Our method reduces expert annotator input by 44%. We present a dataset of 1136 binary segmentation labels approved by board-certified radiologists and make the 143-image validation set public for comparison with other researchers' methods.

CONCLUSIONS: We demonstrate that AL can significantly improve the efficiency and time-effectiveness of creating labeled mammogram datasets. Our framework facilitates the development of high-quality datasets while minimizing manual effort in the domain of digital mammography.

PMID:40415867 | PMC:PMC12094908 | DOI:10.1117/1.JMI.12.3.035501

J Med Imaging (Bellingham). 2025 May;12(3):034501. doi: 10.1117/1.JMI.12.3.034501. Epub 2025 May 21.

ABSTRACT

PURPOSE: Alzheimer's disease (AD) is becoming more prevalent among the elderly, with projections indicating that it will affect a significantly large population in the future. Regardless of substantial research efforts and investments focused on exploring the underlying biological factors, a definitive cure has yet to be discovered. The currently available treatments are only effective in slowing disease progression if it is identified in the early stages of the disease. Therefore, early diagnosis has become critical in treating AD.

APPROACH: Recently, the use of deep learning techniques has demonstrated remarkable improvement in enhancing the precision and speed of automatic AD diagnosis through medical image analysis. We propose a hybrid model that integrates a convolutional variational auto-encoder (CVAE) with a vision transformer (ViT). During the encoding phase, the CVAE captures key features from the MRI scans, whereas the decoding phase reduces irrelevant details in MRIs. These refined inputs enhance the ViT's ability to analyze complex patterns through its multihead attention mechanism.

RESULTS: The model was trained and evaluated using 14,000 structural MRI samples from the ADNI and SCAN databases. Compared with three benchmark methods and previous studies with Alzheimer's classification techniques, our approach achieved a significant improvement, with a test accuracy of 93.3%.

CONCLUSIONS: Through this research, we identified the potential of the CVAE-ViT hybrid approach in detecting minor structural abnormalities related to AD. Integrating unsupervised feature extraction via CVAE can significantly enhance transformer-based models in distinguishing between stages of cognitive impairment, thereby identifying early indicators of AD.

PMID:40415866 | PMC:PMC12094909 | DOI:10.1117/1.JMI.12.3.034501

J Med Imaging (Bellingham). 2025 May;12(3):034502. doi: 10.1117/1.JMI.12.3.034502. Epub 2025 May 22.

ABSTRACT

PURPOSE: Convolutional neural network (CNN)-based models using computed tomography images can classify chronic obstructive pulmonary disease (COPD) with high performance, but various input image types have been investigated, and it is unclear what image types are optimal. We propose a 2D airway-optimized topological multiplanar reformat (tMPR) input image and compare its performance with established 2D/3D input image types for COPD classification. As a secondary aim, we examined the impact of training on a dataset with predominantly mild COPD cases and testing on a more severe dataset to assess whether it improves generalizability.

APPROACH: CanCOLD study participants were used for training/internal testing; SPIROMICS participants were used for external testing. Several 2D/3D input image types were adapted from the literature. In the proposed models, 2D airway-optimized tMPR images (to convey shape and interior/contextual information) and 3D output fusion of axial/sagittal/coronal images were investigated. The area-under-the-receiver-operator-curve (AUC) was used to evaluate model performance and Brier scores were used to evaluate model calibration. To further examine how training dataset severity impacts generalization, we compared model performance when trained on the milder CanCOLD dataset versus the more severe SPIROMICS dataset, and vice versa.

RESULTS: A total of n = 742 CanCOLD participants were used for training/validation and n = 309 for testing; n = 448 SPIROMICS participants were used for external testing. For the CanCOLD and SPIROMICS test set, the proposed 2D tMPR on its own (CanCOLD: AUC = 0.79 ; SPIROMICS: AUC = 0.94 ) and combined with the 3D axial/coronal/sagittal lung view (CanCOLD: AUC = 0.82 ; SPIROMICS: AUC = 0.93 ) had the highest performance. The combined 2D tMPR and 3D axial/coronal/sagittal lung view had the lowest Brier score (CanCOLD: score = 0.16; SPIROMICS: score = 0.24). Conversely, using SPIROMICS for training/testing and CanCOLD for external testing resulted in lower performance when tested on CanCOLD for 2D tMPR on its own (SPIROMICS: AUC = 0.92; CanCOLD: AUC = 0.74) and when combined with the 3D axial/coronal/sagittal lung view (SPIROMICS: AUC = 0.92 ; CanCOLD: AUC = 0.75 ).

CONCLUSIONS: The CNN-based model with the combined 2D tMPR images and 3D lung view as input image types had the highest performance for COPD classification, highlighting the importance of airway information and that the fusion of different types of information as input image can improve CNN-based model performance. In addition, models trained on CanCOLD demonstrated strong generalization to the more severe SPIROMICS cohort, whereas training on SPIROMICS resulted in lower performance when tested on CanCOLD. These findings suggest that training on milder COPD cases may improve classification performance across the disease spectrum.

PMID:40415865 | PMC:PMC12097752 | DOI:10.1117/1.JMI.12.3.034502

J Eval Clin Pract. 2025 Jun;31(4):e70126. doi: 10.1111/jep.70126.

ABSTRACT

BACKGROUND: Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time-consuming, prone to errors, and lacks patient-friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.

OBJECTIVE: The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.

METHODOLOGY: Initially, the retinal images are pre-processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre-processing. Inception-V3, ResNet-152 and a Convolutional Vision Transformer (Conv-ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention-based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.

RESULTS: The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224 × 224 (2019 Data) and the APTOS-2019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.

CONCLUSION: The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.

PMID:40415584 | DOI:10.1111/jep.70126

J Orthop Res. 2025 May 26. doi: 10.1002/jor.26109. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) is revolutionizing orthopedic research and clinical practice by enhancing diagnostic accuracy, optimizing treatment strategies, and streamlining clinical workflows. Recent advances in deep learning have enabled the development of algorithms that detect fractures, grade osteoarthritis, and identify subtle pathologies in radiographic and magnetic resonance images with performance comparable to expert clinicians. These AI-driven systems reduce missed diagnoses and provide objective, reproducible assessments that facilitate early intervention and personalized treatment planning. Moreover, AI has made significant strides in predictive analytics by integrating diverse patient data-including gait and imaging features-to forecast surgical outcomes, implant survivorship, and rehabilitation trajectories. Emerging applications in robotics, augmented reality, digital twin technologies, and exoskeleton control promise to further transform preoperative planning and intraoperative guidance. Despite these promising developments, challenges such as data heterogeneity, algorithmic bias, and the "black box" nature of many models-as well as issues with robust validation-remain. This comprehensive review synthesizes current developments, critically examines limitations, and outlines future directions for integrating AI into musculoskeletal care.

PMID:40415515 | DOI:10.1002/jor.26109

Front Plant Sci. 2025 May 6;16:1543956. doi: 10.3389/fpls.2025.1543956. eCollection 2025.

ABSTRACT

Plant genetic evaluation often faces challenges due to complex genetic structures. Ryegrass (Lolium), a valuable species for pasture-based agriculture, exhibits heterogeneous genetic diversities among base breeding populations. Partially overlapping datasets from incompatible studies and commercial restrictions further impede outcome integration across studies, complicating the evaluation of key agricultural traits such as dry matter yield (DMY). To address these challenges: (1) we implemented a population genotyping approach to capture the genetic diversity in ryegrass base cultivars; (2) we introduced a machine learning-based strategy to integrate genetic distance matrices (GDMs) from incompatible genotyping approaches, including alignments using multidimensional scaling (MDS) and Procrustes transformation, as well as a novel evaluation strategy (BESMI) for the imputation of structural missing data. Endophytes complicate genetic evaluation by introducing additional variation in phenotypic expression. (3) We modelled the impacts of nine commercial endophytes on ryegrass DMY, enabling a more balanced estimation of untested cultivar-endophyte combinations. (4) Phylogenetic analysis provided a pseudo-pedigree relationship of the 113 ryegrass populations and revealed its associations with DMY variations. Overall, this research offers practical insights for integrating partially overlapping GDMs with structural missing data patterns and facilitates the identification of high-performing ryegrass clades. The methodological advancements-including population sequencing, MDS alignment via Procrustes transformation, and BESMI-extend beyond ryegrass applications.

PMID:40416085 | PMC:PMC12100933 | DOI:10.3389/fpls.2025.1543956

Ann Entomol Soc Am. 2025 Feb 22;118(3):229-236. doi: 10.1093/aesa/saaf009. eCollection 2025 May.

ABSTRACT

Targeting the distribution of germ-cell markers is a widely used strategy for investigating germline development in animals. Among these markers, the vasa (vas) orthologues, which encode ATP-dependent RNA helicases, are highly conserved. Previous studies have examined asexual (parthenogenetic) and viviparous embryos of the pea aphid Acyrthosiphon pisum using a cross-reacting Vas antibody. This study utilized a specific antibody against Ap-Vas1, a Vas orthologue in the pea aphid, to gain new insights into germline development. The Ap-Vas1-specific antibody facilitates earlier detection of germ-plasm assembly at the oocyte posterior, challenging the previous assumption that germ-plasm assembly begins only at the onset of embryogenesis. Treatment of oocytes and early embryos with cytoskeleton inhibitors suggests that germ-plasm assembly primarily depends on actin, in contrast to the fly Drosophila melanogaster, where both actin and microtubules are essential. Since pea aphids lack an orthologue of osk, which encodes the protein Osk responsible for anchoring Vas to the germ plasm in Drosophila, this suggests that pea aphids employ distinct mechanisms for osk- and microtubule-independent formation of the germ plasm. Moreover, the clustering of germ cells into germarium-like structures in the extraembryonic region before entering the embryos suggests a gonad formation process different from that in Drosophila, where germ cells begin to cluster into germaria after settling within the embryonic gonads. Therefore, the analysis of the Ap-Vas1 distribution provides a deeper understanding of germline development in asexual pea aphids, uncovering novel aspects of parthenogenetic and viviparous reproduction in insects.

PMID:40415969 | PMC:PMC12095909 | DOI:10.1093/aesa/saaf009

New Phytol. 2025 May 25. doi: 10.1111/nph.70232. Online ahead of print.

ABSTRACT

With the intensification of global warming, there is an urgent need to develop crops with enhanced heat tolerance. Pearl millet, as a typical C4 heat-tolerant crop, has mechanisms of heat tolerance at the cellular level which remain unclear. Constructed single-cell transcriptomic landscape of pearl millet leaves under heat stress and normal conditions, comprising 20 589 high-quality cells classified into five cell types. Vascular tissue cells were identified as the most critical cell type under heat stress, characterized by the highest number of differentially expressed genes and heat stress memory genes. Through single-cell WGCNA analysis combined with phenotypic and physiological analysis of heat stress memory gene UGT73C3 mutants and overexpression lines, we revealed the important role of heat stress memory genes in enhancing heat tolerance by promoting the clearance of reactive oxygen species accumulation. Our study provides a heat-tolerant crop leaf atlas revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

PMID:40415399 | DOI:10.1111/nph.70232

J Chem Inf Model. 2025 May 25. doi: 10.1021/acs.jcim.5c00588. Online ahead of print.

ABSTRACT

The leucine-rich repeat kinase 2 (LRRK2) is the most mutated gene in familial Parkinson's disease, and its mutations lead to pathogenic hallmarks of the disease. The LRRK2 WDR domain is an understudied drug target for Parkinson's disease, with no known inhibitors prior to the first phase of the Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge. A unique advantage of the CACHE Challenge is that the predicted molecules are experimentally validated in-house. Here, we report the design and experimental confirmation of LRRK2 WDR inhibitor molecules. We used an active learning (AL) machine learning (ML) workflow based on optimized free-energy molecular dynamics (MD) simulations utilizing the thermodynamic integration (TI) framework to expand a chemical series around two of our previously confirmed hit molecules. We identified 8 experimentally verified novel inhibitors out of 35 experimentally tested (23% hit rate). These results demonstrate the efficacy of our free-energy-based active learning workflow to explore large chemical spaces quickly and efficiently while minimizing the number and length of expensive simulations. This workflow is widely applicable to screening any chemical space for small-molecule analogs with increased affinity, subject to the general constraints of RBFE calculations. The mean absolute error of the TI MD calculations was 2.69 kcal/mol, with respect to the measured KD of hit compounds.

PMID:40415386 | DOI:10.1021/acs.jcim.5c00588

J Cardiothorac Vasc Anesth. 2025 May 8:S1053-0770(25)00351-9. doi: 10.1053/j.jvca.2025.04.033. Online ahead of print.

ABSTRACT

OBJECTIVES: To predict severe primary graft dysfunction (PGD3) after double-lung transplantation in cystic fibrosis (CF) patients using intraoperative data.

DESIGN: A retrospective single-center cohort study.

SETTING: University Hospital, France.

PARTICIPANTS: CF patients who underwent double-lung transplantation between 2012 and 2019. Patients younger than age 18 and those with multiorgan transplants, retransplantation, or intraoperative cardiopulmonary bypass were excluded.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Sixty-nine variables were recorded in real-time across the nine time-points. PGD3 occurred in 24 patients (15.5%). PGD3 WAS ASSESSED ON POSTOPERATIVE DAY 3: A logistic regression model to predict PGD3 was developed using data collected at nine predefined time-points during surgery, from start (recipient and donor variables) to finish. The model's area under the curve improved progressively during surgery, rising from 0.764 to 0.892. The optimal model incorporated five variables: three associated with reduced PGD3 risk (preoperative pulmonary hypertension, donor body mass index, and PaO₂/FiO₂ ratio at surgery's end) and two were linked to increased risk (lactate level at second pulmonary artery clamping and extracorporeal membrane oxygenation [ECMO] use at surgery's end). The risk of PGD3 increased by a factor of 11.48 (95% CI 4.48-29.39; p < 0.001) when ECMO was required at the end of surgery and by 1.29 (95% CI: 1.02-1.63; p = 0.035) for each 1 mEq/L rise in lactate concentration at time-point 7 (second pulmonary artery clamping).

CONCLUSIONS: This predictive model underscores the adverse impact of sustained ECMO placed at the end of surgery and elevated intraoperative lactate levels on PGD3 risk.

PMID:40414788 | DOI:10.1053/j.jvca.2025.04.033

Respir Med. 2025 May 23:108178. doi: 10.1016/j.rmed.2025.108178. Online ahead of print.

ABSTRACT

BACKGROUND: Elexacaftor/tezacaftor/ivacaftor (ETI) has led to substantial improvements in the clinical outcome of people with cystic fibrosis (pwCF). However, its effects on exercise capacity remain uncertain.

METHODS: This retrospective cohort study included 71 pwCF who started ETI between March 2020 and September 2022. The best performance on the 6-minute walk test (6MWT), defined as the peak walking distance achieved, was compared between the 12 months preceding ETI initiation and the first 14 months of treatment. Pulmonary function tests (PFT) and Cystic Fibrosis Questionnaire-Revised (CFQ-R) were analyzed at treatment initiation and after 12 months.

RESULTS: After starting ETI, the 6MWT was performed at a median interval of 356 [296-380] days. The mean 6-minute walk distance (6MWD) was 641 m ±85.5 at baseline. After treatment, the 6MWD showed a significant absolute increase of 15.8 m (P=0.007). Improvement was greater in pwCF with a percent predicted FEV1 (ppFEV1) ≤40, showing a mean increase of 37.8 m (P=0.009), and in those without prior CFTR modulator therapy with an increase of 21.6 m (P=0.016). After 12 months, the absolute increase in ppFEV1 was 15.8 (P<0.001). The absolute changes from baseline in CFQ-R physical and respiratory scores were 17.9 (P<0.001) and 27 points (P<0.001), respectively. No correlation was found between changes in 6MWT and changes in PFT results.

CONCLUSIONS: ETI improved exercise capacity in pwCF, as evidenced by a significant increase in the 6MWD. ETI was also associated with improvements in physical-related quality of life. Changes in PFT results cannot predict changes in 6MWT results after ETI therapy.

PMID:40414317 | DOI:10.1016/j.rmed.2025.108178

Respir Med. 2025 May 23:108177. doi: 10.1016/j.rmed.2025.108177. Online ahead of print.

ABSTRACT

BACKGROUND: The role of Cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction in non-cystic fibrosis lung diseases, including COPD, is not well understood. The objective of this study was to assess the prevalence of intermediate sweat chloride levels, 30-59 mmol/L, in healthy young adults and the relationship between sweat chloride and pulmonary function.

METHODS: Healthy volunteers > 18 years of age were enrolled in this single center, prospective, cross-sectional pilot study. Sweat chloride testing was performed by pilocarpine iontophoresis. Study participants completed the ATS-DLD LHS-III modified general respiratory symptom questionnaire, spirometry pre- and post- inhaled bronchodilator, and Lung Clearance Index.

RESULTS: 93 subjects were enrolled. 1 subject withdrew and 2 had insufficient sweat volumes collected. Median (IQR) age was 27 years (25, 33) and 40% were male. Median (IQR) sweat chloride was 21 mmol/L (12, 29). 25/90 subjects (28%) had intermediate sweat chloride values, median 37 (33, 40) mmol/L. 60% of individuals with intermediate sweat chloride values were male as compared to 34% of individuals with normal sweat chloride values, p<0.001. Median FEV1 (% predicted) was 100 (90, 109), FEV1/FVC 0.83 (0.81, 0.86), and LCI was 6.01 (5.38, 6.98). There were no differences in pulmonary function between those with normal and intermediate sweat chloride values.

CONCLUSIONS: A significant number of healthy young adults have intermediate sweat chloride levels, but no differences in spirometry and LCI were found. Larger studies, including genetic analyses, are needed to determine if mild CFTR dysfunction impacts respiratory health, especially in older individuals with respiratory co-morbidities.

PMID:40414315 | DOI:10.1016/j.rmed.2025.108177

Fetal Diagn Ther. 2025 May 23:1-12. doi: 10.1159/000546547. Online ahead of print.

ABSTRACT

INTRODUCTION: Cystic fibrosis (CF) is a severe genetic disorder with a carrier frequency of 1 in 30. In Denmark, prenatal testing is offered when there is a family history of CF or ultrasound anomalies suggest an increased risk of the disease. This study evaluates indications for prenatal CF testing and their outcomes.

METHODS: Clinical indications and genetic results were analyzed for pregnancies undergoing CF testing in the Central Denmark Region between August 2012 to 2023. The data were categorized according to clinical indication.

RESULTS: Among 302 prenatal CF tests, echogenic bowel was the most frequent (44.7%, N = 135), leading to identification of one CF-affected fetus (0.7%). The second most common indication was EB together with non-CF-associated ultrasound findings (29.5%, N = 89), with no CF-affected fetuses. Known CF predisposition due to family history (15.6%, N = 47) identified five affected fetuses (10.6%) and 25 carriers (53.2%). No CF cases were detected in other included groups (N = 31).

CONCLUSION: This data shows that echogenic bowel has a low positive predictive value for cystic fibrosis in the fetus (1:230) in a country with combined first trimester screening but no systematic pre-conception or prenatal screening program for cystic fibrosis. Although the relative risk is increased compared to the general population (1:2,500), echogenic bowel appears to be a marker of limited clinical utility. In settings without dedicated CF screening strategies, this underscores the importance of considering the most effective allocation of diagnostic resources.

PMID:40414202 | DOI:10.1159/000546547

Clin Oral Investig. 2025 May 26;29(6):311. doi: 10.1007/s00784-025-06397-z.

ABSTRACT

OBJECTIVE: The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify mandibular asymmetry on head computed tomography (CT) scans.

MATERIALS AND METHODS: A dataset of 368 CT scans, including orthognathic surgery patients, was automatically annotated with 3D cephalometric landmarks via a previously published deep learning-based method. Five of these landmarks were used to automatically construct an MSP orthogonal to the Frankfurt horizontal plane. The reliability of automatic MSP construction was compared with the reliability of manual MSP construction based on 6 manual localizations by 3 experienced operators on 19 randomly selected CT scans. The mandibular asymmetry of the 368 CT scans with respect to the MSP was calculated and compared with clinical expert judgment.

RESULTS: The construction of the MSP was found to be highly reliable, both manually and automatically. The manual reproducibility 95% limit of agreement was less than 1 mm for -y translation and less than 1.1° for -x and -z rotation, and the automatic measurement lied within the confidence interval of the manual method. The automatic MSP construction was shown to be clinically relevant, with the mandibular asymmetry measures being consistent with the expertly assessed levels of asymmetry.

CONCLUSION: The proposed automatic landmark-based MSP construction was found to be as reliable as manual construction and clinically relevant in assessing the mandibular asymmetry of 368 head CT scans.

CLINICAL RELEVANCE: Once implemented in a clinical software, fully automated landmark-based MSP construction could be clinically used to assess mandibular asymmetry on head CT scans.

PMID:40415151 | DOI:10.1007/s00784-025-06397-z