Minerva Med. 2025 Apr;116(2):89-93. doi: 10.23736/S0026-4806.20.06488-5.

ABSTRACT

BACKGROUND: In acute respiratory distress syndrome (ARDS) obesity is associated with lower mortality but the mechanism(s) have not been elucidated.

METHODS: We aimed at assessing plasma biomarker levels interleukin-8 (IL-8), matrix metalloproteinase-7 (MMP-7), Toll-like receptor 2 (TLR-2), tumor necrosis factor-α (TNF-α) and procalcitonin (PCT) at baseline and 3 days later in 20 consecutive moderate-severe ARDS consecutively admitted to our Center.

RESULTS: Our population includes 20 consecutive mechanically ventilated patients with moderate-to severe ARDS. The incidence of obesity was 40% (8/20). No differences were detectable between obese and normal patients in baseline characteristics. In particular, ICU mortality was comparable between the two subgroups. No differences were detectable between the two subgroups at baseline and after 72 hours in biomarker plasma levels. When examining the behavior of each biomarker, obese patients showed a significant increase in MMP7 and TLR-2 values at 72 hours in respect to baseline, differently from normal patients.

CONCLUSIONS: Our data strongly suggest that obese patients with moderate to severe ARDS have an altered inflammatory response to acute lung injury, since a significant increase in MMP-7 and TLR-2 was detectable at 72 hours only in these patients. Further investigations are needed to confirm our results in larger cohorts.

PMID:40387315 | DOI:10.23736/S0026-4806.20.06488-5

Int J Med Sci. 2025 Apr 28;22(10):2434-2445. doi: 10.7150/ijms.114402. eCollection 2025.

ABSTRACT

Aim and background: Vaccines are a cornerstone of modern medicine, significantly reducing morbidity and mortality worldwide. Their administration in infants requires consideration of physiological maturity. Cytochrome P450 (CYP450) enzymes, crucial for drug metabolism, are underdeveloped at birth and mature over the first two to three years of life. While vaccines are not directly metabolized by CYP450 enzymes, emerging evidence suggests that certain excipients-such as polysorbate 80 and gelatin-could interact with CYP450 pathways, particularly in genetically susceptible infants. This study integrates pharmacogenetics and epidemiology to examine how CYP450 immaturity and variability may influence vaccine excipient metabolism, immune activation, and infant health outcomes. Methods: A systematic review of peer-reviewed literature, pharmacogenetic data, and epidemiological studies was conducted to assess CYP450 enzyme activity in infants, potential metabolic interactions with vaccine excipients, and temporal associations between vaccination and sudden infant death syndrome (SIDS). Gaps in postmortem investigations were also evaluated for their impact to identify metabolic vulnerabilities. Results: CYP450 enzymes exhibit developmental immaturity in infants and genetic polymorphisms-particularly in CYP2D6 and CYP3A5-may affect vaccine excipient clearance. While epidemiological evidence shows temporal clustering of some SIDS cases post-vaccination, causality remains unproven. Inflammation-induced suppression of CYP450 enzymes raise questions about potential metabolic vulnerabilities, which current postmortem protocols often fail to capture. Conclusion: This study highlights the need for further research into the influence of CYP450 variability on vaccine-related outcomes. Incorporating genetic and metabolic profiling into postmortem protocols may improve our understanding of metabolic contributions to SIDS and refine vaccine safety assessments. Clinical significance: Developmental immaturity and genetic variability in CYP450 enzymes may affect vaccine excipient metabolism and interact with immune activation. This interplay could influence metabolic vulnerabilities in infants, particularly with inflammation-induced CYP450 suppression. Genetic and metabolic profiling before vaccination could identify at-risk infants, while postmortem analysis may enhance SIDS understanding and vaccine safety assessments.

PMID:40386062 | PMC:PMC12080585 | DOI:10.7150/ijms.114402

Expert Rev Respir Med. 2025 May 19. doi: 10.1080/17476348.2025.2508313. Online ahead of print.

ABSTRACT

INTRODUCTION: Non-cystic-fibrosis bronchiectasis (NCFB) is an airway disorder with a growing world-wide prevalence that affects predominantly older and female individuals and is associated with high symptom burden and significant healthcare expenditure. Brensocatib is a novel orally bioavailable, selective, and reversible dipeptidyl peptidase 1 (DPP1) inhibitor that leads to a sustained inhibition of neutrophil serine protease activity in both whole blood and sputum.

AREAS COVERED: This drug profile summarizes the role of inflammation in the pathophysiology of bronchiectasis. The mechanism of action of brensocatib in reducing neutrophil-related inflammation is described. We then summarize existing efficacy and safety data from Phase 2 and Phase 3 studies of brensocatib in patients with bronchiectasis, in which the rate of exacerbation was the primary endpoint. Finally, we summarize the current marketplace for brensocatib, including the unmet for effective therapies for bronchiectasis, and the status of other potential treatments undergoing clinical trials.

EXPERT OPINION: Brensocatib is the first-in-class DPP1 inhibitor that shows promise as a treatment for patients with bronchiectasis.

PMID:40387478 | DOI:10.1080/17476348.2025.2508313

JPGN Rep. 2025 Mar 17;6(2):146-152. doi: 10.1002/jpr3.70007. eCollection 2025 May.

ABSTRACT

This is a prospective, multicenter study of a cohort of 224 cystic fibrosis (CF) patients treated with CF transmembrane conductance regulator (CFTR) modulators (CFTRm). Our aim was to prospectively analyze the effect of CFTRm treatment on fat-soluble vitamin serum levels. Demographic and clinical data were recorded, and fat-soluble vitamin levels were analyzed at baseline, and at 6 and 12 months after starting treatment. Two groups were analyzed separately: patients receiving dual therapy lumacaftor/ivacaftor or tezacaftor/ivacaftor (Lum/Tez+Iva), and those on triple therapy with elexacaftor/tezacaftor/ivacaftor (ETI). We found that treatment with ETI produced a significant increase in vitamin D and A levels within the first 6 months, which was maintained at 12 months. However, with dual therapy, we observed an increase only in vitamin A levels within the first 6 months, which was not maintained at 12 months. No differences were found in vitamin E serum levels between the groups.

PMID:40386324 | PMC:PMC12078071 | DOI:10.1002/jpr3.70007

JPGN Rep. 2025 Mar 17;6(2):213. doi: 10.1002/jpr3.70011. eCollection 2025 May.

NO ABSTRACT

PMID:40386322 | PMC:PMC12078063 | DOI:10.1002/jpr3.70011

JCEM Case Rep. 2025 May 15;3(7):luaf104. doi: 10.1210/jcemcr/luaf104. eCollection 2025 Jul.

ABSTRACT

Glucagon-like peptide-1 (GLP-1) agonists are widely used in the management of type 2 diabetes and obesity, with their therapeutic scope expanding to address cardiometabolic and cardiorenal conditions. However, their increasing use has been associated with potential adverse effects, including acute pancreatitis (AP). The exact prevalence of GLP-1 agonist-induced AP remains uncertain and reliable predictors for its onset have yet to be identified. We present 3 cases of class-associated predilection for GLP-1 analog-associated AP in patients with carrier states for hemochromatosis (HC) and cystic fibrosis. Case 1 is a heterozygous carrier for the C282Y HC pathogenic variant. Case 2 is a heterozygous carrier of the Delta F508 deletion of the cystic fibrosis transmembrane regulator (CFTR) gene. Case 3 is compound heterozygous carrier of a single CFTR intron 9 poly T allele pathogenic variant (5T/7T/8T), as well as a single pathogenic variant of the C282Y HC gene. Our observation suggests that carrier states for cystic fibrosis and HC may predispose individuals to GLP-1 agonist-associated AP. Genetic testing for these carrier states should be considered among patients with GLP-1 agonist-associated AP to provide more support and data for this as a potential true risk factor.

PMID:40384889 | PMC:PMC12078934 | DOI:10.1210/jcemcr/luaf104

Quant Imaging Med Surg. 2025 May 1;15(5):4431-4444. doi: 10.21037/qims-24-2205. Epub 2025 Apr 28.

ABSTRACT

BACKGROUND: Pulmonary magnetic resonance imaging (MRI) has the advantage of nonionizing radiation and multiparameter imaging of structure and function, facilitating its clinical use in a variety of pulmonary diseases. This study aimed to identify the research trends and emerging topics in pulmonary MRI by conducting a comprehensive bibliometric analysis of the field over the past decade.

METHODS: A search of the Web of Science Core Collection database was conducted with the words "lung" and "MRI" for literature published from 2014 to 2023. The data were further analyzed with R and CiteSpace software in terms of annual publications and citations, collaborative networks (countries, institutions, and authors), source's local impact, keyword clustering, and burst analysis.

RESULTS: A total of 1,839 publications related to pulmonary MRI have been published over the last decade, with a relatively slow growth trend. The top three journals in terms of total publications and citations were Magnetic Resonance in Medicine, Journal of Magnetic Resonance Imaging, and Radiology. The most productive country was the United States, and the countries with the strongest collaborative links were the United States and the United Kingdom. The most productive institutions and authors were Ruprecht Karls University Heidelberg (articles, n=309) and Wild JM (articles, n=86), respectively. Keyword cluster analysis identified five clusters: "lung cancer", "magnetic resonance imaging", "lung MRI", "cystic fibrosis", and "congenital diaphragmatic hernia". Keyword burst analysis showed that the keywords with the highest burst intensity in the first 5 years and the last 5 years were "mice" and "standardization", respectively.

CONCLUSIONS: Over the past decade, research trends in pulmonary MRI have focused on lung cancer and cystic fibrosis as the dominant clinical diseases. Research has been centered on standardizing pulmonary MRI to promote its clinical application.

PMID:40384665 | PMC:PMC12082580 | DOI:10.21037/qims-24-2205

Bioinformatics. 2025 May 19:btaf165. doi: 10.1093/bioinformatics/btaf165. Online ahead of print.

ABSTRACT

MOTIVATION: Deep learning has deeply influenced protein science, enabling breakthroughs in predicting protein properties, higher-order structures, and molecular interactions.

RESULTS: This paper introduces DeepProtein, a comprehensive and user-friendly deep learning library tailored for protein-related tasks. It enables researchers to seamlessly address protein data with cutting-edge deep learning models. To assess model performance, we establish a benchmark that evaluates different deep learning architectures across multiple protein-related tasks, including protein function prediction, subcellular localization prediction, protein-protein interaction prediction, and protein structure prediction. Furthermore, we introduce DeepProt-T5, a series of fine-tuned Prot-T5-based models that achieve state-of-the-art performance on four benchmark tasks, while demonstrating competitive results on six of others. Comprehensive documentation and tutorials are available which could ensure accessibility and support reproducibility.

AVAILABILITY AND IMPLEMENTATION: Built upon the widely used drug discovery library DeepPurpose, DeepProtein is publicly available at https://github.com/jiaqingxie/DeepProtein.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40388205 | DOI:10.1093/bioinformatics/btaf165

Interdiscip Cardiovasc Thorac Surg. 2025 May 19:ivaf101. doi: 10.1093/icvts/ivaf101. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoSR software, which provides 3D visualizations of patient-specific anatomy, potentially enhancing a surgeon's understanding of the lung structure.

METHODS: A dataset of 125 CT scans from lung segmentectomy patients at Erasmus MC was used. Manual annotations for pulmonary arteries and veins were created with 3D Slicer. nnU-Net models were trained for both lungs, assessed using Dice score, sensitivity, and specificity. Intraoperative recordings demonstrated clinical applicability. A paired t-test evaluated statistical significance of the differences between automatic and manual segmentations.

RESULTS: The nnU-Net model, trained at full 3D resolution, achieved a mean Dice score between 0.91 and 0.92. The mean sensitivity and specificity were: left artery: 0.86 and 0.99, right artery: 0.84 and 0.99, left vein: 0.85 and 0.99, right vein: 0.85 and 0.99. The automatic method reduced segmentation time from ∼1.5 hours to under 5 min. Five cases were evaluated to demonstrate how the segmentations support lung segmentectomy procedures. P-values for Dice scores were all below 0.01, indicating statistical significance.

CONCLUSIONS: The nnU-Net models successfully performed automatic segmentation of pulmonary arteries and veins in both lungs. When integrated with visualization tools, these automatic segmentations can enhance preoperative and intraoperative planning by providing detailed 3D views of patients anatomy.

PMID:40388152 | DOI:10.1093/icvts/ivaf101

ACS Nano. 2025 May 19. doi: 10.1021/acsnano.5c04340. Online ahead of print.

ABSTRACT

Conventional point-of-care testing (POCT) has limitations in sensitivity with high risks of missed detection or false positive, which restrains its applications for routine outpatient care analysis and early clinical diagnosis. By merits of the cutting-edge quantum precision metrology, this study devised a mini quantum sensor via magnetofluorescent qubit tagging and tunning on core-shelled fluorescent nanodiamond FND@SiO2. Comprehensive characterizations confirmed the formation of FND biolabels, while spectroscopies secured no degradation in spin-state transition after surface modification. A methodical parametrization was deliberated and decided, accomplishing a wide-field modulation depth ≥15% in ∼ zero field, which laid foundation for supersensitive sensing at single-FND resolution. Using viral nucleocapsid protein as a model marker, an ultralow limit of detection (LOD) was obtained by lock-in analysis, outperforming conventional colorimetry and immunofluorescence by > 2000 fold. Multianalyte and affinity assays were also enabled on this platform. Further by resort to artificial-intelligence (AI) augmentation in the Unet-ConvLSTM-Attention architecture, authentic qubit dots were identified by pixelwise survey through pristine qubit queues. Such processing not just improved pronouncedly the probing precision but also achieved deterministic detections down to a single protein in human saliva with an ultimate LOD as much as 7800-times lower than that of colloidal Au approach, which competed with the RT-qPCR threshold and the certified critical value of SIMOA, the gold standard. Hence, by AI-aided digitization on optic qubits, this REASSURED-compliant contraption may promise a next-generation POCT solution with unparalleled sensitivity, speed, and cost-effectiveness, which in whole confers a conclusive proof of the prowess of the burgeoning quantum metrics in biosensing.

PMID:40388114 | DOI:10.1021/acsnano.5c04340

Afr J Reprod Health. 2025 May 16;29(5s):51-64. doi: 10.29063/ajrh2025/v29i5s.7.

ABSTRACT

This study built a model to forecast the maturity of lungs by blending radiomics and deep learning methods. We examined ultrasound images from 263 pregnancies in the pregnancy stages. Utilizing the GE VOLUSON E8 system we captured images to extract and analyze radiomic features. These features were integrated with clinical data by means of deep learning algorithms such as DenseNet121 to enhance the accuracy of assessing fetal lung maturity. This combined model was validated by receiver operating characteristic (ROC) curve, calibration diagram, as well as decision curve analysis (DCA). We discovered that the accuracy and reliability of the diagnosis indicated that this method significantly improves the level of prediction of fetal lung maturity. This novel non-invasive diagnostic technology highlights the potential advantages of integrating diverse data sources to enhance prenatal care and infant health. The study lays groundwork, for validation and refinement of the model across various healthcare settings.

PMID:40387939 | DOI:10.29063/ajrh2025/v29i5s.7

Langmuir. 2025 May 19. doi: 10.1021/acs.langmuir.5c00769. Online ahead of print.

ABSTRACT

The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl groups per nanoparticle by combining anisotropically collapsing agarose gels for nanoparticle immobilization with fluorescence microscopy and acid-base titration. We applied this approach to photon-upconversion nanoparticles (UCNPs) coated with poly(acrylic acid) (PAA) and fluorescence-labeled polystyrene nanoparticles (PNs), which serve as models for bioimaging and environmental pollutants, respectively. UCNPs exhibited 152 ± 14 thousand carboxyl groups per particle (∼11 groups/nm2), while PNs were characterized with 38 ± 3.6 thousand groups (∼1.7 groups/nm2). The limit of detection was 6.4 and 1.9 thousand carboxyl groups per nanoparticle, and the limit of quantification was determined at 21 and 6.2 thousand carboxyl groups per nanoparticle for UCNP-PAAs and PNs, respectively. High intrinsic luminescence enabled direct imaging of UCNPs, while PNs required fluorescence staining with Nile Red to overcome low signal-to-noise ratios. The study also discussed the critical influence of nanoparticle concentration and titration conditions on the assay performance. This method advances the precise characterization of surface chemistry, offering insights into nanoparticle structure that extend beyond the resolution of electron microscopy. Our findings establish a robust platform for investigating the interplay of surface chemistry with nanoparticle function and fate in technological and environmental contexts, with broad applicability across nanomaterials.

PMID:40387864 | DOI:10.1021/acs.langmuir.5c00769

JASA Express Lett. 2025 May 1;5(5):053601. doi: 10.1121/10.0036754.

ABSTRACT

The increasing needs for controlling high noise levels motivate development of automatic sound event detection and classification methods. Little work deals with automatic train pass-by detection despite a high degree of annoyance. To this matter, an innovative approach is proposed in this paper. A generic classifier identifies vehicle noise on the raw audio signal. Then, combined short sound level analysis and mel-spectrogram-based classification refine this outcome to discard anything but train pass-bys. On various long-term signals, a 90% temporal overlap with reference demarcation is observed. This high detection rate allows a proper railway noise contribution estimation in different soundscapes.

PMID:40387613 | DOI:10.1121/10.0036754

J Cell Mol Med. 2025 May;29(10):e70564. doi: 10.1111/jcmm.70564.

ABSTRACT

Leukaemia stem cells (LSCs) are a rare population among the bulk of leukaemia cells and are responsible for disease initiation, progression/relapse and insensitivity to therapies in numerous haematologic malignancies. Identification of LSCs and monitoring of their quantity before, during, and after treatments will provide a guidance for choosing a correct treatment and assessing therapy response and disease prognosis, but such a method is still lacking simply because there are no distinct morphological features recognisable for distinguishing LSCs from normal stem cell counterparts. Using artificial intelligence (AI) deep learning and polycythemia vera (PV) as a disease model (a type of human myeloproliferative neoplasms derived from a haematopoietic stem cell harbouring the JAK2V617F oncogene), we combine 19 convolutional neural networks as a whole to build AI models for analysing single-cell images, allowing for distinguishing between LSCs from JAK2V617F knock-in mice and normal stem counterparts from healthy mice with a high accuracy (> 99%). We prove the concept that LSCs possess unique morphological features compared to their normal stem cell counterparts, and AI, but not microscopic visualisation by pathologists, can extract and identify these features. In addition, we show that LSCs and other cell lineages in PV mice are also distinguishable by AI. Our study opens up a potential AI morphology field for identifying various primitive leukaemia cells, especially including LSCs, to help assess therapy responses and disease prognosis in the future.

PMID:40387596 | DOI:10.1111/jcmm.70564

Med Phys. 2025 May 19. doi: 10.1002/mp.17885. Online ahead of print.

ABSTRACT

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, potential collisions between the treatment couch and the on-board imaging system limit the range that the gantry can be rotated. Limited-angle measurements are often insufficient to generate high-quality volumetric images for image-domain registration, therefore limiting the use of CBCT for position verification. An alternative to image-domain registration is to use a few 2D projections acquired by the onboard kV imager to register with the 3D planning CT for patient position verification, which is referred to as 2D-3D registration.

PURPOSE: The 2D-3D registration involves converting the 3D volume into a set of digitally reconstructed radiographs (DRRs) expected to be comparable to the acquired 2D projections. The domain gap between the generated DRRs and the acquired projections can happen due to the inaccurate geometry modeling in DRR generation and artifacts in the actual acquisitions. We aim to improve the efficiency and accuracy of the challenging 2D-3D registration problem in non-coplanar RT with limited-angle CBCT scans.

METHOD: We designed an accelerated, dataset-free, and patient-specific 2D-3D registration framework based on an implicit neural representation (INR) network and a composite similarity measure. The INR network consists of a lightweight three-layer multilayer perception followed by average pooling to calculate rigid motion parameters, which are used to transform the original 3D volume to the moving position. The Radon transform and imaging specifications at the moving position are used to generate DRRs with higher accuracy. We designed a composite similarity measure consisting of pixel-wise intensity difference and gradient differences between the generated DRRs and acquired projections to further reduce the impact of their domain gap on registration accuracy. We evaluated the proposed method on both simulation data and real phantom data acquired from a Varian TrueBeam machine. Comparisons with a conventional non-deep-learning registration approach and ablation studies on the composite similarity measure were conducted to demonstrate the efficacy of the proposed method.

RESULTS: In the simulation data experiments, two X-ray projections of a head-and-neck image with 45 ∘ ${45}^\circ$ discrepancy were used for the registration. The accuracy of the registration results was evaluated on experiments set up at four different moving positions with ground-truth moving parameters. The proposed method achieved sub-millimeter accuracy in translations and sub-degree accuracy in rotations. In the phantom experiments, a head-and-neck phantom was scanned at three different positions involving couch translations and rotations. We achieved translation errors of < 2 mm $< 2\nobreakspace {\rm mm}$ and subdegree accuracy for pitch and roll. Experiments on registration using different numbers of projections with varying angle discrepancies demonstrate the improved accuracy and robustness of the proposed method, compared to both the conventional registration approach and the proposed approach without certain components of the composite similarity measure.

CONCLUSION: We proposed a dataset-free lightweight INR-based registration with a composite similarity measure for the challenging 2D-3D registration problem with limited-angle CBCT scans. Comprehensive evaluations of both simulation data and experimental phantom data demonstrated the efficiency, accuracy, and robustness of the proposed method.

PMID:40387508 | DOI:10.1002/mp.17885

J Sleep Res. 2025 May 19:e70090. doi: 10.1111/jsr.70090. Online ahead of print.

ABSTRACT

Parasomnias are abnormal behaviours or mental experiences during sleep or the sleep-wake transition. As disorders of arousal (DOA) or REM sleep behaviour disorder (RBD) can be difficult to capture in the sleep laboratory and may need to be diagnosed in large communities, new home diagnostic devices are being developed, including actigraphy, EEG headbands, as well as 2D infrared and 3D time of flight home cameras (often with automatic analysis). Traditional video-polysomnographic diagnostic criteria for RBD and DOA are becoming more accurate, and deep learning methods are beginning to accurately classify abnormal polysomnographic signals in these disorders. Big data from vast collections of clinical, cognitive, brain imaging, DNA and polysomnography data have provided new information on the factors that are associated with parasomnia and, in the case of RBD, may predict the individual risk of conversion to an overt neurodegenerative disease. Dream engineering, including targeted reactivation of memory during sleep, combined with image repetition therapy and lucid dreaming, is helping to alleviate nightmares in patients. On a political level, RBD has brought together specialists in abnormal movements and sleep neurologists, and research into nightmares and sleep-wake dissociations has brought together sleep and consciousness scientists.

PMID:40387303 | DOI:10.1111/jsr.70090

Int Forum Allergy Rhinol. 2025 May 19:e23595. doi: 10.1002/alr.23595. Online ahead of print.

ABSTRACT

BACKGROUND: The assessment of eosinophilic chronic rhinosinusitis (eCRS) lacks accurate non-invasive preoperative prediction methods, relying primarily on invasive histopathological sections. This study aims to use computed tomography (CT) images and clinical parameters to develop an integrated deep learning model for the preoperative identification of eCRS and further explore the biological basis of its predictions.

METHODS: A total of 1098 patients with sinus CT images were included from two hospitals and were divided into training, internal, and external test sets. The region of interest of sinus lesions was manually outlined by an experienced radiologist. We utilized three deep learning models (3D-ResNet, 3D-Xception, and HR-Net) to extract features from CT images and calculate deep learning scores. The clinical signature and deep learning score were inputted into a support vector machine for classification. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used to evaluate the integrated deep learning model. Additionally, proteomic analysis was performed on 34 patients to explore the biological basis of the model's predictions.

RESULTS: The area under the curve of the integrated deep learning model to predict eCRS was 0.851 (95% confidence interval [CI]: 0.77-0.93) and 0.821 (95% CI: 0.78-0.86) in the internal and external test sets. Proteomic analysis revealed that in patients predicted to be eCRS, 594 genes were dysregulated, and some of them were associated with pathways and biological processes such as chemokine signaling pathway.

CONCLUSIONS: The proposed integrated deep learning model could effectively predict eCRS patients. This study provided a non-invasive way of identifying eCRS to facilitate personalized therapy, which will pave the way toward precision medicine for CRS.

PMID:40387008 | DOI:10.1002/alr.23595

Adv Ther. 2025 May 19. doi: 10.1007/s12325-025-03215-6. Online ahead of print.

ABSTRACT

The latest clinical practice guidelines for idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF) were jointly published by the American Thoracic Society (ATS), European Respiratory Society (ERS), Japanese Respiratory Society (JRS), and Asociacion Latinoamericana de Thorax (ALAT) in 2022, and a new term-"PPF"-has been proposed to describe patients with non-IPF fibrosing interstitial lung diseases (ILDs), with defined criteria. However, the proposal of this new term has caused confusion amongst experts at a time when use of the term "progressive fibrosing interstitial lung disease" (PF-ILD), proposed in the phase 3 INBUILD trial of nintedanib, has become widely adopted by pulmonologists and rheumatologists in clinical practice. In this commentary, we discuss the background and concepts underpinning the terms PPF and PF-ILD and seek to provide pulmonologists and rheumatologists with a deeper understanding of the concept of PPF.

PMID:40388091 | DOI:10.1007/s12325-025-03215-6

N Engl J Med. 2025 May 18. doi: 10.1056/NEJMoa2414108. Online ahead of print.

ABSTRACT

BACKGROUND: Nerandomilast (BI 1015550) is an orally administered preferential inhibitor of phosphodiesterase 4B with antifibrotic and immunomodulatory effects. In a phase 2 trial involving patients with idiopathic pulmonary fibrosis, treatment with nerandomilast stabilized lung function over a period of 12 weeks.

METHODS: In this phase 3, double-blind trial, we randomly assigned patients with idiopathic pulmonary fibrosis in a 1:1:1 ratio to receive nerandomilast at a dose of 18 mg twice daily, nerandomilast at a dose of 9 mg twice daily, or placebo, with stratification according to background antifibrotic therapy (nintedanib or pirfenidone vs. none). The primary end point was the absolute change from baseline in forced vital capacity (FVC), measured in milliliters, at week 52.

RESULTS: A total of 1177 patients underwent randomization, of whom 77.7% were taking nintedanib or pirfenidone at enrollment. Adjusted mean changes in FVC at week 52 were -114.7 ml (95% confidence interval [CI], -141.8 to -87.5) in the nerandomilast 18-mg group, -138.6 ml (95% CI, -165.6 to -111.6) in the nerandomilast 9-mg group, and -183.5 ml (95% CI, -210.9 to -156.1) in the placebo group. The adjusted difference between the nerandomilast 18-mg group and the placebo group was 68.8 ml (95% CI, 30.3 to 107.4; P<0.001), and the adjusted difference between the nerandomilast 9-mg group and the placebo group was 44.9 ml (95% CI, 6.4 to 83.3; P = 0.02). The most frequent adverse event in the nerandomilast groups was diarrhea, reported in 41.3% of the 18-mg group and 31.1% of the 9-mg group, as compared with 16.0% in the placebo group. Serious adverse events were balanced across trial groups.

CONCLUSIONS: In patients with idiopathic pulmonary fibrosis, treatment with nerandomilast resulted in a smaller decline in the FVC than placebo over a period of 52 weeks. (Funded by Boehringer Ingelheim; FIBRONEER-IPF ClinicalTrials.gov number, NCT05321069.).

PMID:40387033 | DOI:10.1056/NEJMoa2414108

J Crit Care Med (Targu Mures). 2025 Apr 30;11(2):112-121. doi: 10.2478/jccm-2025-0013. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Interstitial lung disease (ILD) is a cluster of diseases that affect the lungs, characterized by different degrees of inflammation and fibrosis within the parenchyma. In the intensive care unit (ICU), ILD poses substantial challenges because of its complicated nature and high morbidity and mortality rates in severe cases. ILD pathophysiology frequently entails persistent inflammation that results in fibrosis, disrupting the typical structure and function of the lung. Patients with ILD frequently experience dyspnea, non-productive cough, and tiredness. In the ICU setting, these symptoms may worsen and lead to signs of acute respiratory failure with significantly impaired gas physiology.

METHODOLOGY: A systematic search was conducted in reputable databases, including PubMed, Google Scholar, and Embase. To ensure a comprehensive search, a combination of keywords such as "interstitial lung disease," "intensive care," and "outcomes" was used. Studies published within the last ten years reporting on the outcomes of ILD patients admitted to intensive care included.

RESULT: Effective management of ILD in an ICU setting is challenging and requires a comprehensive approach to address the triggering factor and providing respiratory support, Hypoxemia severity is a critical predictor of mortality, with lower PaO2/FiO2 ratios during the first three days of ICU admission associated with increased mortality rates. The need for mechanical ventilation, particularly invasive mechanical ventilation (IMV), is a significant predictor of poor outcomes in ILD patients. Additionally, higher positive end-expiratory pressure (PEEP) settings, and severity of illness scores, such as the Acute Physiology and Chronic Health Evaluation (APACHE) score, are also linked to increased mortality. Other poor prognostic factors include the presence of shock and pulmonary fibrosis on computed tomography (CT) images. Among the various types of ILDs, idiopathic pulmonary fibrosis (IPF) is associated with the highest mortality rate. Furthermore, a high ventilatory ratio (VR) within 24 hours after intubation independently predicts ICU mortality.

CONCLUSION: This literature review points out outcome predictors of interstitial lung disease in intensive care units, which are mainly hypoxemia, the severity of the illness, invasive ventilation, the presence of shock, and the extent of fibrosis on CT Images.

PMID:40386698 | PMC:PMC12080564 | DOI:10.2478/jccm-2025-0013