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

Front Bioeng Biotechnol. 2025 May 2;13:1597387. doi: 10.3389/fbioe.2025.1597387. eCollection 2025.

ABSTRACT

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and lung cancer, exhibit elevated death rates and pathological intricacy, requiring advancements that surpass the constraints of traditional therapies. This study comprehensively outlines the novel applications of nanomaterials in respiratory medicine by accurately modulating the pulmonary mechanical microenvironment, encompassing alveolar surface tension, extracellular matrix rigidity, and the immune-fibroblast interaction network. The precise delivery, stimuli-responsive characteristics, and biomimetic design of nanomaterials markedly improve drug concentration at the lesion site and mitigate fibrosis, inflammation, and malignant tumor advancement by disrupting mechanical signaling pathways. The study clarifies their multifaceted benefits in treating COPD, IPF, and lung cancer, including decreased systemic toxicity and improved spatiotemporal control. Nonetheless, clinical translation continues to encounter obstacles, including impediments in large-scale production, inadequate compatibility with breathing devices, and disputes concerning long-term biosafety. In the future, the amalgamation of precision medicine, adaptive smart materials, and multi-omics artificial intelligence technologies will facilitate the development of individualized diagnostic and therapeutic systems, establishing a novel paradigm for the proactive management of respiratory disorders. This review offers essential theoretical foundations and technical approaches for the practical application of nanomaterials and the enhancement of therapeutic techniques in respiratory medicine.

PMID:40386463 | PMC:PMC12081457 | DOI:10.3389/fbioe.2025.1597387

Front Pharmacol. 2025 May 2;16:1506518. doi: 10.3389/fphar.2025.1506518. eCollection 2025.

ABSTRACT

INTRODUCTION: Senescent cells (SCs) accumulate with age and play a causative role in age-related diseases, such as idiopathic pulmonary fibrosis (IPF). Clearance of SCs attenuates lung fibrogenesis and favors fibrosis resolution, suggesting that targeting of SCs is recognized as a promising therapeutic approach for IPF. Isothiocyanates (ITCs) are natural compounds with anticancer and anti-aging properties, but their role in IPF remains unclear. The aim of our study to investigate whether benzyl isothiocyanate (BITC), a type of ITCs, can act as a senolytic agent thereby attenuating pulmonary fibrosis in aged mice.

METHODS: Primary lung fibroblasts from IPF patients and controls were cultured and treated with various ITCs to identify potential senolytic agents. Senescence-associated β-galactosidase staining, Cell viability assays, Annexin V/PI double staining, Caspase 3 activity assay, Western blot analysis, and qPCR were performed to evaluate senescence markers, cell viability, and apoptosis-related proteins after BITC treatment of senescent IPF lung fibroblasts in vitro. HE staining, Masson staining, Hydroxyproline assay, and Western blot analysis were used to assess the pathological progress, collagen content of lung tissues, and fibrotic gene expression changes after BITC treatment in C57BL/6 aged mice.

RESULTS: Using senescent IPF fibroblasts, we screened and identified BITC as a potent senolytic drug. We show that BITC selectively induces apoptosis in senescent IPF fibroblasts by targeting AKT signal pathway. Intraperitoneal administration of BITC to an age-related lung fibrosis mouse model effectively depleted senescent lung fibroblasts and reversed persistent pulmonary fibrosis.

DISCUSSION: Our study reveals that BITC may be a promising therapeutic option for IPF and other age-related disease that progress with the accumulation of senescent fibroblasts.

PMID:40385483 | PMC:PMC12081420 | DOI:10.3389/fphar.2025.1506518

World J Clin Cases. 2025 May 16;13(14):98769. doi: 10.12998/wjcc.v13.i14.98769.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) has a poor prognosis if left untreated; therefore, early treatment with pirfenidone is crucial. Lei et al conducted a retrospective analysis to evaluate the effectiveness of early pirfenidone treatment on lung function in 113 patients with IPF. In addition to other research, pirfenidone has demonstrated efficacy in patients at all stages of IPF once correct diagnosis has been made. In advanced IPF, we include the requirement for pirfenidone. Therefore, it is essential to choose an appropriate method of administration method, such as inhalation. This may circumvent the drawbacks of the high cost and possible adverse effects of this drug.

PMID:40385293 | PMC:PMC11752435 | DOI:10.12998/wjcc.v13.i14.98769

Acta Neuropathol. 2025 May 19;149(1):49. doi: 10.1007/s00401-025-02889-0.

ABSTRACT

Trisomy of chromosome 21, the cause of Down syndrome (DS), is the most commonly occurring genetic cause of Alzheimer's disease (AD). Here, we compare the frontal cortex proteome of people with Down syndrome-Alzheimer's disease (DSAD) to demographically matched cases of early onset AD and healthy ageing controls. We find dysregulation of the proteome, beyond proteins encoded by chromosome 21, including an increase in the abundance of the key AD-associated protein, APOE, in people with DSAD compared to matched cases of AD. To understand the cell types that may contribute to changes in protein abundance, we undertook a matched single-nuclei RNA-sequencing study, which demonstrated that APOE expression was elevated in subtypes of astrocytes, endothelial cells, and pericytes in DSAD. We further investigate how trisomy 21 may cause increased APOE. Increased abundance of APOE may impact the development of, or response to, AD pathology in the brain of people with DSAD, altering disease mechanisms with clinical implications. Overall, these data highlight that trisomy 21 alters both the transcriptome and proteome of people with DS in the context of AD, and that these differences should be considered when selecting therapeutic strategies for this vulnerable group of individuals who have high risk of early onset dementia.

PMID:40387921 | DOI:10.1007/s00401-025-02889-0

J Exp Med. 2025 Aug 4;222(8):e20242078. doi: 10.1084/jem.20242078. Epub 2025 May 19.

ABSTRACT

Latent viral infections rely on a precise coordination of the immune response to control sporadic viral reactivation. CD8+ T cells play a crucial role in controlling viral latency by generating diverse memory responses in an epitope-specific manner. Among these distinct responses, conventional and inflationary memory responses have been described during herpesvirus infections. Using a newly generated TCR transgenic mouse strain, we investigated the transcriptomic and epigenetic remodeling of distinct epitope-specific CD8+ T cells during CMV infection across tissues at both population and single-cell levels. Our findings reveal that whereas the transcriptomic and epigenetic landscapes of conventional and inflationary memory responses diverge in the spleen and liver, these molecular programs converge in the salivary gland, a site of CMV persistence. Thus, we provide evidence that the dynamics of memory CD8+ T cell responses are distinct between tissues.

PMID:40387857 | DOI:10.1084/jem.20242078

Elife. 2025 May 19;13:RP101630. doi: 10.7554/eLife.101630.

ABSTRACT

Microglia exhibit diverse morphologies reflecting environmental conditions, maturity, or functional states. Thus, morphological characterization provides important information to understand microglial roles and functions. Most recent morphological analysis relies on classifying cells based on morphological parameters. However, this classification may lack biological relevance, as microglial morphologies represent a continuum rather than distinct, separate groups, and do not correspond to mathematically defined clusters irrelevant of microglial cells function. Instead, we propose a new open-source tool, MorphoCellSorter, which assesses microglial morphology by automatically computing morphological criteria, using principal component analysis and Andrews plots to score cells. MorphoCellSorter properly ranked cells from various microglia datasets in mice and rats of different ages, from in vivo, in vitro, and ex vivo models, that were acquired using diverse imaging techniques. This approach allowed for the discrimination of cell populations in various pathophysiological conditions. Finally, MorphoCellSorter offers a versatile, easy, and ready-to-use method to evaluate microglial morphological diversity that could easily be generalized to standardize practices across laboratories.

PMID:40387080 | DOI:10.7554/eLife.101630

NPJ Womens Health. 2025;3(1):31. doi: 10.1038/s44294-025-00077-9. Epub 2025 May 15.

ABSTRACT

This study employed physiologically based pharmacokinetic (PBPK) modelling to compare the extent of foetal exposure between oral and long-acting injectable (LAI) aripiprazole and olanzapine. Adult and pregnancy PBPK models were developed and validated with relevant clinical data. Relevant indices of foetal exposure during pregnancy were predicted from concentration-time data at steady-state dosing for both oral and LAI formulations. Foetal Cmax for aripiprazole was 59-78% higher with LAI than oral, and 68-181% higher with LAI olanzapine than the oral formulation. Predicted cord:maternal ratios (range) were 0.59-0.69 for oral aripiprazole and 0.61-0.66 for LAI aripiprazole, 0.34-0.64 for oral olanzapine and 0.89-0.96 for LAI olanzapine. Also, cumulative foetal exposure over 28 days from oral formulations were generally predicted to be lower compared with their therapeutic-equivalent LAI. As in utero foetal exposure to maternal drugs does not necessarily translate to risk, these data should be interpreted in a broader context that includes benefit-risk assessments.

PMID:40386696 | PMC:PMC12081286 | DOI:10.1038/s44294-025-00077-9