J Biol Chem. 2025 May 31:110329. doi: 10.1016/j.jbc.2025.110329. Online ahead of print.

ABSTRACT

Cellular protein homeostasis relies on a complex network of protein synthesis, folding, sub-cellular localization, and degradation to sustain a functional proteome. Since, most of these processes are energy driven, proteostasis is inescapably afflicted by cellular metabolism. Proteostasis collapse and metabolic imbalance are both linked to aging and age-associated disorders, yet they have traditionally been studied as a separate phenomenon in the context of aging. In this study, we indicate that reduced proteostasis capacity is a result of a metabolic imbalance associated with age. We observed increased accumulation of L-serine and L-threonine in replicative old cells of S. cerevisiae, indicating an imbalance in amino acid metabolism with replicative aging. Replicating this metabolic imbalance in young cells through deletion of serine dependent transcriptional activator, CHA4, resulted in increased aggregation of endogenous proteins along with misfolding prone proteins Guk1-7ts-GFP and Luciferase-GFP in both young and old cells. Aggregate formation in the cha4Δ strain required a functional sensor of mitochondrial dysfunction and an activator of the retrograde signalling gene, RTG2. CHA4 and RTG2 exhibited genetic interaction and together regulated mitochondrial metabolism, replicative lifespan, and aggregate formation in young cells, connecting metabolic regulation with proteostasis and aging. Constitutive activation of retrograde signalling through overexpression of RTG2 or deletion of MKS-1, negative regulator of Rtg1-Rtg3 nuclear translocation, resulted in faster resolution of aggregates upon heat shock through RTG3 and was found to be independent of molecular chaperone upregulation.

PMID:40456447 | DOI:10.1016/j.jbc.2025.110329

PLoS One. 2025 Jun 2;20(6):e0321864. doi: 10.1371/journal.pone.0321864. eCollection 2025.

ABSTRACT

Factor V deficiency is a congenital coagulation disorder characterized by the absence or malfunction of factor V (FV). The purpose of this study was to develop a viable FV-deficient mouse model using CRISPR/Cas9 technology. A viable pathological model of the disease was not available to develop new therapies. A previous in silico study was performed to select a mutation causing a mild disease phenotype in humans (Thr1898Met missense). Such mutation was replicated in mice by CRISPR-mediated homology directed repair. Following crossing, homozygous individuals were subjected to coagulometry assays, including FV levels, prothrombin time (PT), and activated partial thromboplastin time (aPTT). The in silico study suggested that the mutation destabilizes FV structure of both mouse and human variants, putatively producing a mild phenotype of the disease in mice. Mendelian inheritance was observed in the offspring. No spontaneous signs of blood clotting disturbances, premature deaths or gestational dysfunctions were observed. FV levels in homozygous animals were 24.5% ± 5.1; 39.7 sec ± 2.8; PT was 61.8% ± 6.3; 23.4 sec ± 1.6 (INR = 1.47 ± 0.12); and aPTT was 46.9 sec ± 3.2. A viable FV-deficient mouse model was generated by introducing a missense mutation in FV. The model exhibits a mild phenotype of the disease, akin to that observed in humans.

PMID:40455764 | DOI:10.1371/journal.pone.0321864

IEEE Trans Med Imaging. 2025 Jun 2;PP. doi: 10.1109/TMI.2025.3575402. Online ahead of print.

ABSTRACT

Deep learning-based disease grading technologies facilitate timely medical intervention due to their high efficiency and accuracy. Recent advancements have enhanced grading performance by incorporating the ordinal relationships of disease labels. However, existing methods often assume same probability distributions for disease labels across instances within the same category, overlooking variations in label distributions. Additionally, the hyperparameters of these distributions are typically determined empirically, which may not accurately reflect the true distribution. To address these limitations, we propose a disease grading network utilizing a sample-aware asymmetric Gaussian label distribution, termed DGN-AGLD. This approach includes a variance predictor designed to learn and predict parameters that control the asymmetry of the Gaussian distribution, enabling distinct label distributions within the same category. This module can be seamlessly integrated into standard deep learning networks. Experimental results on four disease datasets validate the effectiveness and superiority of the proposed method, particularly on the IDRiD dataset, where it achieves a diabetic retinopathy accuracy of 77.67%. Furthermore, our method extends to joint disease grading tasks, yielding superior results and demonstrating significant generalization capabilities. Visual analysis indicates that our method more accurately captures the trend of disease progression by leveraging the asymmetry in label distribution. Our code is publicly available on https://github.com/ahtwq/AGNet.

PMID:40456095 | DOI:10.1109/TMI.2025.3575402

IEEE Trans Neural Netw Learn Syst. 2025 Jun 2;PP. doi: 10.1109/TNNLS.2025.3566647. Online ahead of print.

ABSTRACT

Visual recognition in low-light environments is a challenging problem since degraded images are the stacking of multiple degradations (noise, low light and blur, etc.). It has received extensive attention from academia and industry in the era of deep learning. Existing surveys focus on low-light image enhancement (LLIE) methods and normal-light visual recognition methods, while few comprehensive surveys of low-light-related vision tasks. This article provides a comprehensive survey of the latest advancements in low-light vision, including methods, datasets, and evaluation metrics, in two aspects: visual quality-driven and recognition quality-driven. On the visual quality-driven aspect, we survey a large number of very recent LLIE methods. On the recognition quality-driven aspect, we survey low-light object detection techniques in the deep learning era using more intuitive categorization method. Furthermore, a quantitative benchmarking of different methods is conducted on several widely adopted low-light vision-related datasets. Finally, we discuss the challenges that exist in low-light vision and future directions worth exploring. We provide a public website that will continue to track developments in this promising field.

PMID:40456083 | DOI:10.1109/TNNLS.2025.3566647

J Chem Inf Model. 2025 Jun 2. doi: 10.1021/acs.jcim.5c00584. Online ahead of print.

ABSTRACT

In the realm of medicinal chemistry, the predominant challenge in molecular design lies in managing extensive molecular data sets and effectively screening for, as well as preserving, molecules with potential value. Traditional methodologies typically utilize deep learning models or genetic algorithms (GA) for optimization, yet each approach has inherent limitations: deep learning models are constrained by substantial computational resource demands; genetic algorithms often yield molecular structures with low validity and feasibility. To overcome these challenges, we have developed the Molecular multiobjective optimization of Monte Carlo Tree Search (MCTS) and Non-Superiority Ranking Genetic Algorithm II (NSGA-II)-MNopt, which ingeniously integrates MCTS with NSGA-II. Specifically, NSGA-II demonstrates unique strengths in balancing multiple optimization objectives and achieves rapid performance through its crowding distance and nondominated ordering mechanisms, while MCTS focuses on enhancing the validity of molecular structures to ensure that the generated molecules are both desirable and feasible. Notably, MNopt does not require reliance on extensive molecular training data sets in the initial stages, effectively mitigating excessive resource consumption. Experimental results demonstrate that MNopt surpasses existing techniques in multiobjective optimization, generating effective and diverse molecular structures, thereby offering a crucial tool for novel drug discovery and materials science.

PMID:40456025 | DOI:10.1021/acs.jcim.5c00584

PLoS One. 2025 Jun 2;20(6):e0320701. doi: 10.1371/journal.pone.0320701. eCollection 2025.

ABSTRACT

Advancements in deep learning have revolutionized numerous real-world applications, including image recognition, visual question answering, and image captioning. Among these, image captioning has emerged as a critical area of research, with substantial progress achieved in Arabic, Chinese, Uyghur, Hindi, and predominantly English. However, despite Urdu being a morphologically rich and widely spoken language, research in Urdu image captioning remains underexplored due to a lack of resources. This study creates a new Urdu Image Captioning Dataset (UCID) called UC-23-RY to fill in the gaps in Urdu image captioning. The Flickr30k dataset inspired the 159,816 Urdu captions in the dataset. Additionally, it suggests deep learning architectures designed especially for Urdu image captioning, including NASNetLarge-LSTM and ResNet-50-LSTM. The NASNetLarge-LSTM and ResNet-50-LSTM models achieved notable BLEU-1 scores of 0.86 and 0.84 respectively, as demonstrated through evaluation in this study accessing the model's impact on caption quality. Additionally, it provides useful datasets and shows how well-suited sophisticated deep learning models are for improving automatic Urdu image captioning.

PMID:40455832 | DOI:10.1371/journal.pone.0320701

PLoS One. 2025 Jun 2;20(6):e0320732. doi: 10.1371/journal.pone.0320732. eCollection 2025.

ABSTRACT

BACKGROUND: Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Traditional diagnostic methods have significant limitations in terms of accuracy and consistency. Imaging is a common technique for diagnosing and predicting breast cancer, but human error remains a concern. Increasingly, artificial intelligence (AI) is being employed to assist physicians in reducing diagnostic errors.

METHODS: We developed an intelligent diagnostic model combining deep learning and radiomics to enhance breast tumor diagnosis. The model integrates MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, improving feature processing and efficiency while reducing parameters. Using AI-Dhabyani and TCIA breast ultrasound datasets, we validated the model internally and externally, comparing it to VGG16, ResNet, AlexNet, and MobileNet. Results: The internal validation set achieved an accuracy of 83.84% with an AUC of 0.92, outperforming other models. The external validation set showed an accuracy of 69.44% with an AUC of 0.75, demonstrating high robustness and generalizability. Conclusions: We developed an intelligent diagnostic model using deep learning and radiomics to improve breast tumor diagnosis. The model combines MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, enhancing feature processing and efficiency while reducing parameters. It was validated internally and externally using the AI-Dhabyani and TCIA breast ultrasound datasets and compared with VGG16, ResNet, AlexNet, and MobileNet.

PMID:40455816 | DOI:10.1371/journal.pone.0320732

PLoS One. 2025 Jun 2;20(6):e0323440. doi: 10.1371/journal.pone.0323440. eCollection 2025.

ABSTRACT

Intestinal sounds, primarily generated by the movement of digested gas and liquids during peristalsis, are acoustic signals that provide valuable insights into intestinal functioning. Traditionally, doctors have relied on stethoscopes to assess the degree of gastrointestinal activity. Recent advancements in computer-aided technologies and electronic stethoscopes have enhanced the understanding and analysis of these sounds. Studies utilizing advanced techniques like deep learning and convolutional neural networks have shown promise in analyzing bowel sounds. Nevertheless, the reliance on personal judgment and the need for large labeled datasets limit the broader applicability of these methods. This study introduces an innovative, unsupervised grading system to objectively evaluate gastrointestinal motility by analyzing bowel sounds through spectral feature analysis. This system offers a practical alternative to traditional listening techniques or complex models. It computes an activity score for digital audio using a cost-effective numerical grading method to assist doctors in quantifying gastrointestinal motility. The method's reliability, validated by Spearman's rank correlation, confirms its accuracy in assessing activity levels and highlights its potential as a reliable and practical tool for supporting objective medical assessments of bowel activity.

PMID:40455773 | DOI:10.1371/journal.pone.0323440

PLoS One. 2025 Jun 2;20(6):e0325177. doi: 10.1371/journal.pone.0325177. eCollection 2025.

ABSTRACT

Recent developments in artificial intelligence (AI) have introduced new technologies that can aid in detecting cognitive decline. This study developed a voice-based AI model that screens for cognitive decline using only a short conversational voice sample. The process involved collecting voice samples, applying machine learning (ML), and confirming accuracy through test data. The AI model extracts multiple voice features from the collected voice data to detect potential signs of cognitive impairment. Data labeling for ML was based on Mini-Mental State Examination scores: scores of 23 or lower were labeled as "cognitively declined (CD)," while scores above 24 were labeled as "cognitively normal (CN)." A fully coupled neural network architecture was employed for deep learning, using voice samples from 263 patients. Twenty voice samples, each comprising a one-minute conversation, were used for accuracy evaluation. The developed AI model achieved an accuracy of 0.950 in discriminating between CD and CN individuals, with a sensitivity of 0.875, specificity of 1.000, and an average area under the curve of 0.990. This voice AI model shows promise as a cognitive screening tool accessible via mobile devices, requiring no specialized environments or equipment, and can help detect CD, offering individuals the opportunity to seek medical attention.

PMID:40455724 | DOI:10.1371/journal.pone.0325177

PLoS One. 2025 Jun 2;20(6):e0325261. doi: 10.1371/journal.pone.0325261. eCollection 2025.

ABSTRACT

With the advancement of sensor technologies such as LiDAR and depth cameras, the significance of three-dimensional point cloud data in autonomous driving and environment sensing continues to increase.Point cloud registration stands as a fundamental task in constructing high-precision environmental models, with particular significance in overlapping regions where the accuracy of feature extraction and matching directly impacts registration quality. Despite advancements in deep learning approaches, existing methods continue to demonstrate limitations in extracting comprehensive features within these overlapping areas. This study introduces an innovative point cloud registration framework that synergistically combines the K-nearest neighbor (KNN) algorithm with a channel attention mechanism (CAM) to significantly enhance feature extraction and matching capabilities in overlapping regions. Additionally, by designing an effectiveness scoring network, the proposed method improves registration accuracy and enhances system robustness in complex scenarios. Comprehensive evaluations on the ModelNet40 dataset reveal that our approach achieves markedly superior performance metrics, demonstrating significantly lower root mean square error (RMSE) and mean absolute error (MAE) compared to established methods including iterative closest point (ICP), Robust & Efficient Point Cloud Registration using PointNet (PointNetLK), Go-ICP, fast global registration (FGR), deep closest point (DCP), self-supervised learning for a partial-to-partial registration (PRNet), and Iterative Distance-Aware Similarity Matrix Convolution (IDAM). This performance advantage is consistently maintained across various challenging conditions, including unseen shapes, novel categories, and noisy environments. Furthermore, additional experiments on the Stanford dataset validate the applicability and robustness of the proposed method for high-precision 3D shape registration tasks.

PMID:40455723 | DOI:10.1371/journal.pone.0325261

PLoS One. 2025 Jun 2;20(6):e0324496. doi: 10.1371/journal.pone.0324496. eCollection 2025.

ABSTRACT

Quantitative MRI (qMRI) requires the acquisition of multiple images with parameter changes, resulting in longer measurement times than conventional imaging. Deep learning (DL) for image reconstruction has shown a significant reduction in acquisition time and improved image quality. In qMRI, where the image contrast varies between sequences, preparing large, fully-sampled (FS) datasets is challenging. Recently, methods that do not require FS data such as self-supervised learning (SSL) and zero-shot self-supervised learning (ZSSSL) have been proposed. Another challenge is the large GPU memory requirement for DL-based qMRI image reconstruction, owing to the simultaneous processing of multiple contrast images. In this context, Kellman et al. proposed memory-efficient learning (MEL) to save the GPU memory. This study evaluated SSL and ZSSSL frameworks with MEL to accelerate qMRI. Three experiments were conducted using the following sequences: 2D T2 mapping/MSME (Experiment 1), 3D T1 mapping/VFA-SPGR (Experiment 2), and 3D T2 mapping/DESS (Experiment 3). Each experiment used the undersampled k-space data under acceleration factors of 4, 8, and 12. The reconstructed maps were evaluated using quantitative metrics. In this study, we performed three qMRI reconstruction measurements and compared the performance of the SL- and GT-free learning methods, SSL and ZSSSL. Overall, the performances of SSL and ZSSSL were only slightly inferior to those of SL, even under high AF conditions. The quantitative errors in diagnostically important tissues (WM, GM, and meniscus) were small, demonstrating that SL and ZSSSL performed comparably. Additionally, by incorporating a GPU memory-saving implementation, we demonstrated that the network can operate on a GPU with a small memory (<8GB) with minimal speed reduction. This study demonstrates the effectiveness of memory-efficient GT-free learning methods using MEL to accelerate qMRI.

PMID:40455714 | DOI:10.1371/journal.pone.0324496

J Appl Clin Med Phys. 2025 Jun 2:e70124. doi: 10.1002/acm2.70124. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to develop a CycleGAN based denoising model to enhance the quality of low-dose PET (LDPET) images, making them as close as possible to standard-dose PET (SDPET) images.

METHODS: Using a Philips Vereos PET/CT system, whole-body PET images of fluorine-18 fluorodeoxyglucose (18F-FDG) were acquired from 37 patients to facilitate the development of the UR-CycleGAN model. In this model, low-dose data were simulated by reconstructing PET images with a 30-s acquisition time, while standard-dose data were reconstructed from a 2.5-min acquisition. The network was trained in a supervised manner on 13 210 pairs of PET images, and the quality of the images was objectively evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

RESULTS: Compared to simulated low-dose data, the denoised PET images generated by our model showed significant improvement, with a clear trend toward SDPET image quality.

CONCLUSION: The proposed method reduces acquisition time by 80% compared to standard-dose imaging, while achieving image quality close to SDPET images. It also enhances visual detail fidelity, demonstrating the feasibility and practical utility of the model for significantly reducing imaging time while maintaining high image quality.

PMID:40455649 | DOI:10.1002/acm2.70124

PLoS Genet. 2025 Jun 2;21(6):e1011261. doi: 10.1371/journal.pgen.1011261. Online ahead of print.

ABSTRACT

The myometrium, the muscular layer of the uterus, undergoes crucial transitions during pregnancy, maintaining quiescence throughout gestation, and generating coordinated contractions during labor. Dysregulation of this transition can lead to premature labor with serious complications for the infant. Despite extensive gene expression data available for varying myometrial states, the molecular mechanisms governing the increase in contraction-associated gene expression at labor onset remain unclear. Transcription factors, such as JUND and progesterone receptor (PR), play essential roles in regulating transcription of select myometrial contraction-associated genes, however, a broader understanding of their involvement in transcriptional regulation at a genome-wide scale is lacking. This study examines changes in transcription and JUND binding within human myometrial tissue during the transition from quiescence (term-not-in labor/TNIL) to contractility (term labor/TL). Total RNA-sequencing reveals a global increase in primary transcript levels at TL, with AP-1/JUND binding motifs overrepresented in the promoters of upregulated transcripts. Interestingly, ChIP-seq analysis demonstrates higher JUND enrichment in TNIL compared to TL tissues, suggesting its role in preparing the myometrium for labor onset. Integration of JUND and PR ChIP-seq data identifies over 10,000 gene promoters bound by both factors at TNIL and TL, including genes involved in labor-driving processes. Additionally, the study uncovers elevated levels of enhancer RNAs (eRNAs) at intergenic JUND peaks in laboring myometrial tissues, and implicates additional transcription factors, such as NFKB and ETS, in the regulatory switch from quiescence to contractility. In summary, this research enhances our understanding of the myometrial molecular regulatory network during pregnancy and labor, shedding light on the roles of JUND and PR in gene expression regulation genome-wide. These findings open avenues for further exploration, potentially leading to improved interventions for preventing premature labor and the associated complications.

PMID:40455848 | DOI:10.1371/journal.pgen.1011261

Discov Oncol. 2025 Jun 2;16(1):987. doi: 10.1007/s12672-025-02321-9.

ABSTRACT

BACKGROUND: Dysregulation of cholesterol metabolism has been recognized as a critical driver in the pathogenesis of hepatic disorders, particularly hepatocellular carcinoma (HCC) and metabolic dysfunction-associated steatotic liver disease (MASLD). However, the causal relationships between circulating proteins involved in cholesterol homeostasis and the progression of these hepatopathologies remain insufficiently explored, warranting further mechanistic investigation.

METHODS: This study utilized Mendelian randomization (MR) to identify the role of cholesterol metabolism-related proteins in HCC and MASLD. We systematically investigated the causal associations of these proteins with HCC and MASLD and their roles in disease progression using circulating proteomic databases and bioinformatics tools. In addition, network-based drug repositioning techniques and molecular docking experiments were utilized to assess the interactions of the above biomarkers with known drugs to discover drugs with potential therapeutic effects.

RESULTS: MR analysis identified several proteins linked with significant risk for HCC and MASLD. Notably, apolipoprotein E (APOE) expression was significantly increased in tissues from HCC and MASLD patients, closely correlating with elevated disease risk. Meta-analysis demonstrated a significant causal relationship between APOE and increased risk of HCC (OR: 1.710, 95% CI 1.220-2.400; P < 0.01) and MASLD (OR: 1.490, 95% CI 1.280-1.740; P < 0.01). Additionally, network analysis revealed extensive interactions between APOE and other disease-related proteins, suggesting that APOE may contribute to liver disease progression through its influence on complex protein networks.

CONCLUSION: Our findings delineate a novel mechanistic involvement of cholesterol regulatory proteins, with APOE demonstrating pathogenic significance in both HCC and MASLD. This investigation substantially provides new insights into the molecular mechanisms of these liver diseases and highlights potential therapeutic targets.

PMID:40455174 | DOI:10.1007/s12672-025-02321-9

Immunotherapy. 2025 Jun 2:1-6. doi: 10.1080/1750743X.2025.2510892. Online ahead of print.

ABSTRACT

Chronic spontaneous urticaria (CSU) is a complex inflammatory skin condition that significantly impacts patients' quality of life. Conventional treatments, such as antihistamines, often fail to provide adequate symptom control. The next step involves administering omalizumab, a monoclonal antibody targeting IgE, however, a subset of patients may not respond to this treatment underscoring the necessity for alternative treatment options. Remibrutinib, an oral, selective inhibitor of Bruton's tyrosine kinase (BTK), has emerged as a promising therapy in CSU. BTK is vital for the activation of mast cells and basophils. The inhibitory action of remibrutinib on BTK may help alleviate CSU symptoms by addressing mast cell-mediated inflammation. Clinical trials, including Phase II and III studies, have shown promising efficacy and a favorable safety profile for remibrutinib in treating CSU. Patients experienced rapid symptom relief, with notable improvements in the Urticaria Activity Score (UAS7) concerning both itch intensity and the severity of hives. The safety profile was also commendable, with no significant treatment-related adverse events requiring therapy cessation or posing immediate health risks to patients. These results indicate that remibrutinib may become a preferred oral treatment for patients with moderate to severe CSU who do not adequately respond to standard therapies.

PMID:40455080 | DOI:10.1080/1750743X.2025.2510892

Am J Respir Crit Care Med. 2025 Jun 2. Online ahead of print.

ABSTRACT

RATIONALE: Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) was shown to be safe and efficacious in children 6 through 11 years of age with cystic fibrosis (CF) and at least one F508del allele in a 24-week phase 3 study. Children completing this study could enroll into a 192-week extension study.

OBJECTIVES: Evaluate long-term safety and efficacy of ELX/TEZ/IVA in children ≥6 years.

METHODS: In this 2-part (Part A [96-weeks] and Part B [96-weeks]) phase 3 extension study, children <12 years weighing <30 kg received ELX 100 mg once daily (qd)/TEZ 50 mg qd/IVA 75 mg every 12 hours (q12h) and children weighing ≥ 30 kg or aged ≥12 years received ELX 200 mg qd/TEZ 100 mg qd/IVA 150 mg q12h.

MEASUREMENTS AND MAIN RESULTS: Sixty-four children (F/MF [n=36] and F/F [n=28]) received ≥ 1 dose of ELX/TEZ/IVA. Mean exposure was 156.2 weeks and 60.9% of children (n=39) completed treatment in both parts of this 192-week study. The primary endpoint was safety. All children had adverse events (AEs), which for most were mild (31.3%) or moderate (64.1%) and generally consistent with common manifestations of CF. Two children (3.1%) had non-serious AEs that lead to treatment discontinuation (increased alanine aminotransferase [n=1] and aggression [n=1]). Secondary endpoints focused on efficacy. From parent study baseline, improvements were seen in ppFEV1 (9.6 percentage points; 95% CI: 5.4, 13.7), sweat chloride concentration (-57.9 mmol/L; 95% CI: -63.3, -52.5), CFQ-R respiratory domain score (10.0 points; 95% CI: 6.9, 13.0), LCI2.5 (-2.33; 95% CI: -2.87, -1.79), and BMI z-score (0.39; 95% CI: 0.19, 0.59) at Week 192. Rate of pulmonary exacerbations per year was 0.05. The annualized rate of change in ppFEV1 and LCI2.5 was -0.09 percentage points (95% CI: -1.01, 0.84) and -0.07 units (95%CI: -0.12, -0.01), respectively.

CONCLUSIONS: In this 4-year extension study in children ≥6 years, the longest clinical trial experience with a CFTR modulator in this pediatric population, ELX/TEZ/IVA remained generally safe and well-tolerated with no new safety findings. Clinically meaningful improvements in lung function, CFTR function, and nutritional status reported in the parent study were maintained. These results confirm the long-term safety and efficacy of ELX/TEZ/IVA in children ≥6 years. Clinical trial registration available at www.

CLINICALTRIALS: gov, ID: NCT04183790.

PMID:40454869

Health Expect. 2025 Jun;28(3):e70312. doi: 10.1111/hex.70312.

ABSTRACT

BACKGROUND: Engaging patients and family members in healthcare quality improvement (QI) is essential to meet the needs of those who receive care. The objective of this study was to describe the experience of patient/family partners (PFPs) in a national QI collaborative and to develop recommendations for best practices for patient engagement.

METHODS: We conducted focus groups with PFPs in a national QI collaborative focused on improving transitions of care between cystic fibrosis (CF) and lung transplant programmes. Audio recordings were transcribed verbatim, coded inductively and analysed through thematic analysis. Member checking with PFPs, clinicians and team coaches was used to refine the findings and develop recommendations.

RESULTS: Five PFPs participated in two focus groups, which revealed that PFPs (1) were motivated to participate as members of the QI team because they felt deeply connected to their CF care teams and wanted to help other patients, (2) felt engaged in the QI collaborative and appreciated the sense of community, support from the team coach and the opportunity to take ownership of projects, and (3) suggested improvements related to timing of meetings, compensation, being mindful when discussing sensitive information and setting clear expectations. Member checking revealed the need for equitable recruitment processes and tailoring the role to the individual participants. The findings were used to develop and change processes in the collaborative.

CONCLUSIONS: The structure of the national QI collaborative supported patient/family partnership through structured meetings and a focus on building relationships of mutual respect. The findings demonstrated the need for a more equitable recruitment process, better expectation setting and customisation of the role to the individual skills, needs and preferences of the participants.

PATIENT OR PUBLIC CONTRIBUTION: Patients and family members of people living with CF participated in this study through focus groups and member checking. One CF patient (B.D.) is a co-author of this paper and contributed to data analysis, sensemaking, writing and editing.

PMID:40454855 | DOI:10.1111/hex.70312

J Am Chem Soc. 2025 Jun 2. doi: 10.1021/jacs.5c04167. Online ahead of print.

ABSTRACT

The Burkholderia cepacia complex (BCC) is a group of Gram-negative bacteria known for their pathogenicity to patients suffering from cystic fibrosis (CF). The BCC-belonging strain B. pyrrocinia BC11 (formerly B. cepacia BC11) produces AFC-BC11, a compound with strong activity against phytopathogenic fungi. In this contribution, we report on the unprecedented N-acyl-tetrapeptide structure and antifungal potency of this natural product. We further provide insights into central steps of its biosynthesis mediated by a nonclassical nonribosomal peptide synthesis machinery lacking condensation domains. With the involvement of a sole acyl/peptidyl carrier protein AfcK, an acyltransferase AfcL and coenzyme A, the growing acyl-peptide chain is shuffled between different thioester carriers during the intricate biosynthetic assembly. The knowledge of the AFC-BC11 structure may contribute to the development of antifungals against phytopathogens and, with the afc gene cluster being conserved in various Burkholderia strains, possibly to an understanding of the human pathogenesis of the BCC.

PMID:40454803 | DOI:10.1021/jacs.5c04167

Am J Physiol Lung Cell Mol Physiol. 2025 Jun 2. doi: 10.1152/ajplung.00009.2025. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a chronic disease caused by dysfunctional or absent cystic fibrosis transmembrane conductance regulator (CFTR). CFTR is expressed in immune cells and regulates innate immunity, both directly and indirectly. The epithelial sodium channel (ENaC) contributes to dysfunction in CF airway epithelial cells. However, the impact of non-CFTR ion channel dysfunction on CF immune responses is not understood. Improved understanding of how immune function is regulated by ion channels may allow antibiotic- and mutation-agnostic treatment approaches to chronic infection and inflammation. Therefore, we hypothesized that ENaC is aberrantly expressed in CF macrophages and directly contributes to impaired phagocytic and inflammatory functions. ENaC expression was characterized in immune cells isolated from CF and non-CF blood donors. Monocyte-derived macrophage (MDM) function and bacterial killing was tested with ENaC modulation. Baseline ENaC expression in human CF MDMs, lymphocytes, and granulocytes was increased at both the transcript and protein level relative to non-CF and persisted after infection. CFTR inhibition in non-CF MDMs resulted in ENaC overexpression. CFTR modulator treatment reduced but did not eliminate ENaC overexpression in CF MDMs. Interestingly, ENaC inhibition increased CFTR expression. Amiloride-treated CF MDMs also showed normalized ROS production, improved autophagy, and decreased pro-inflammatory cytokine production. Sodium channel expression in CF MDMs normalized after Amiloride treatment with minimal effect on other ion channels. In summary, ENaC modulation in immune cells is a novel potential therapeutic target for CF infection control, either in combination with CFTR modulators, or as a sole agent for people not eligible for CFTR modulators.

PMID:40454714 | DOI:10.1152/ajplung.00009.2025

J Chem Inf Model. 2025 Jun 2. doi: 10.1021/acs.jcim.5c00556. Online ahead of print.

ABSTRACT

The black-box nature of deep learning has increasingly drawn attention to the reliability and uncertainty of predictive models. Currently, several uncertainty quantification (UQ) methods have been proposed and successfully applied in the fields of molecules and proteins, effectively improving model prediction quality and interpretability. Protein-RNA binding represents a fundamental aspect of protein research. Accurate prediction of binding sites and ensuring the reliability of such predictions are crucial for various scientific endeavors. However, many of the existing computational methods have a single feature extraction and lack of UQ. To address these, we propose MGCA (multiscale graph convolutional networks, convolutional neural networks and attention) to better capture local and global information and achieve competitive results in predicting protein-RNA binding sites. Moreover, we launch a UQ study based on MGCA and five prevalent models to verify the robustness of the results. Specifically, we introduce the Expected Calibration Error (ECE) to assess the uncertainty of the models. Additionally, a novel split-bins screening method is proposed based on the ECE, aiming to investigate the practical impact of reducing uncertainty on the models. Finally, temperature scaling (TS) is used to calibrate model uncertainty without changing performance. Results show that the split-bins screening method reduces false positives (FP), and TS significantly decreases the model ECE. The split-bins screening method combined with TS can further reduce FP and improve precision. Our findings demonstrate that TS effectively reduces uncertainty in protein-RNA binding site prediction, and minimizing model uncertainty enhances prediction quality. The data and code can be available at https://github.com/trustcm/UQ-TS-Split-bins-RBP.

PMID:40455481 | DOI:10.1021/acs.jcim.5c00556