FEBS J. 2025 Mar 3. doi: 10.1111/febs.70050. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a genetic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, leading to chronic, unresolved inflammation of the airways due to uncontrolled recruitment of polymorphonuclear leukocytes (PMNs). Evidence indicates that CFTR loss-of-function, in addition to promoting a pro-inflammatory phenotype, is associated with an increased risk of developing cancer, suggesting that CFTR can exert tumor-suppressor functions. Three-dimensional (3D) in vitro culture models, such as the CF lung airway-on-a-chip, can be suitable for studying PMN recruitment, as well as events of cancerogenesis, that is epithelial cell invasion and migration, in CF. To gather insight into the pathobiology of CFTR loss-of-function, we generated CFTR-knockout (KO) clones of the 16HBE14o- human bronchial cell line by CRISPR/Cas9 gene editing, and performed a comparative proteomic analysis of these clones with their wild-type (WT) counterparts. Systematic signaling pathway analysis of CFTR-KO clones revealed modulation of inflammation, PMN recruitment, epithelial cell migration, and epithelial-mesenchymal transition. Using a latest-generation organ-on-a-chip microfluidic platform, we confirmed that CFTR-KO enhanced PMN recruitment and epithelial cell invasion of the endothelial layer. Thus, a dysfunctional CFTR affects multiple pathways in the airway epithelium that ultimately contribute to sustained inflammation and cancerogenesis in CF.

PMID:40029006 | DOI:10.1111/febs.70050

Phys Occup Ther Pediatr. 2025 Mar 3:1-16. doi: 10.1080/01942638.2025.2469567. Online ahead of print.

ABSTRACT

AIMS: To evaluate the effects of telerehabilitation (TG) compared with an unsupervised home exercise training program (HG) on muscle function, physical activity (PA), and sleep in children with cystic fibrosis (CF).

METHODS: Thirty children with CF (mean age = 10.2 ± 1.9 years) were randomly allocated to TG or HG. The exercise protocol was applied thrice a week for six weeks in the TG via Skype. The same exercises were sent in an exercise booklet to the HG, and phone contact was made once a week. Muscle function (one-minute sit-to-stand (1-min STS), sit-up, pushup, squat, and plank tests)), PA (Physical Activity Questionnaire for Older Children), and sleep (Epworth Sleepiness Scale (ESS) and Pediatric Sleep Questionnaire (PSQ)) were assessed before and after the 6-week study period.

RESULTS: The 1-min STS significantly improved in the TG compared with the HG (p ≤ .001, ηp2 = 0.474). The sit-up (p = .005, ηp2 = 0.247), pushup (p = .002, ηp2 = 0.180), squat (p = .002, ηp2 = 0.284), and plank (p < .001, ηp2 = 0.360) test scores were significantly improved in the TG compared to the HG. No significant changes between groups were seen for PA (p = .261, ηp2 = 0.045), ESS (p = .160, ηp2 = 0.069), or PSQ (p = .763, ηp2 = 0.003).

CONCLUSION: Children who received TG improved muscle function more than children who received an HG. The effectiveness of longer term TG programs should be investigated in children with CF.

PMID:40028780 | DOI:10.1080/01942638.2025.2469567

Genet Med Open. 2025 Jan 7;3:101962. doi: 10.1016/j.gimo.2025.101962. eCollection 2025.

ABSTRACT

PURPOSE: Newborn screening identifies rare diseases that result from the recessive inheritance of pathogenic variants in both copies of a gene. Long-read genome sequencing (LRS) is used for identifying and phasing genomic variants, but further efforts are needed to develop LRS for applications using low-yield DNA samples.

METHODS: In this study, genomic DNA with high molecular weight was obtained from 2 cystic fibrosis patients, comprising a whole-blood sample (CF1) and a newborn dried blood spot sample (CF2). Library preparation and genome sequencing (30-fold coverage) were performed using 20 ng of DNA input on both the PacBio Revio system and the Illumina NovaSeq short-read sequencer. Single-nucleotide variants, small indels, and structural variants were identified for each data set.

RESULTS: Our results indicated that the genotype concordance between long- and short-read genome sequencing data was higher for single-nucleotide variants than for small indels. Both technologies accurately identified known pathogenic variants in the CFTR gene (CF1: p.(Met607_Gln634del), p.(Phe508del); CF2: p.(Phe508del), p.(Ala455Glu)) with complete concordance for the polymorphic poly-TG and consecutive poly-T tracts. Using PacBio read-based haplotype phasing, we successfully determined the allelic phase and identified compound heterozygosity of pathogenic variants at genomic distances of 32.4 kb (CF1) and 10.8 kb (CF2).

CONCLUSION: Haplotype phasing of rare pathogenic variants from minimal DNA input is achieved through LRS. This approach has the potential to eliminate the need for parental testing, thereby shortening the time to diagnosis in genetic disease screening.

PMID:40027236 | PMC:PMC11869909 | DOI:10.1016/j.gimo.2025.101962

Obstet Med. 2025 Feb 26:1753495X251319588. doi: 10.1177/1753495X251319588. Online ahead of print.

ABSTRACT

BACKGROUND: Elexacaftor/tezacaftor/ivacaftor (ETI) was made available to eligible women in September 2020 by NHS Scotland.

METHODS: Retrospective data collection for the 13 pregnancies in women taking ETI from the West of Scotland Adult Cystic Fibrosis Unit, September 2020-December 2023.

RESULTS: Mean pre-pregnancy FEV1 was 2.26L, 70% predicted (range 1.25-3.19); (38-86% predicted). Mean FEV1 post-pregnancy was 2.29L, 71% predicted (range 1.49-3.40); (45-92% predicted). The mean age at conception (29 years) and mean percentage predicted FEV1 (70%) were higher than in other UK studies. Two pregnancies resulted in miscarriage, the remaining 11 pregnancies resulted in a live birth. Seven women had a pulmonary exacerbation of CF during pregnancy. Three of four women with FEV1 < 60% predicted had uncomplicated pregnancies with no pulmonary exacerbations.

CONCLUSION: We demonstrate that people with CF and varying spectrums of lung disease who take CFTR modulators can have uncomplicated pregnancies with positive lung function outcomes.

PMID:40027072 | PMC:PMC11866333 | DOI:10.1177/1753495X251319588

Curr Med Sci. 2025 Mar 3. doi: 10.1007/s11596-025-00017-3. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Disease (PD) using handwritten spiral and wave images, and to compare its performance with various machine learning (ML) and deep learning (DL) models.

METHODS: The study utilized a dataset of 204 images (102 spiral and 102 wave) from PD patients and healthy subjects. The images were preprocessed using the Histogram of Oriented Gradients (HOG) descriptor and augmented to increase dataset diversity. The DNN model was designed with an input layer, three convolutional layers, two max-pooling layers, two dropout layers, and two dense layers. The model was trained and evaluated using metrics such as accuracy, sensitivity, specificity, and loss. The DNN model was compared with nine ML models (random forest, logistic regression, AdaBoost, k-nearest neighbor, gradient boost, naïve Bayes, support vector machine, decision tree) and two DL models (convolutional neural network, DenseNet-201).

RESULTS: The DNN model outperformed all other models in diagnosing PD from handwritten spiral and wave images. On spiral images, the DNN model achieved accuracies of 41.24% over naïve Bayes, 31.24% over decision tree, and 27.9% over support vector machine. On wave images, the DNN model achieved accuracies of 40% over naïve Bayes, 36.67% over decision tree, and 30% over support vector machine. The DNN model demonstrated significant improvements in sensitivity and specificity compared to other models.

CONCLUSIONS: The DNN model significantly improves the accuracy of PD diagnosis using handwritten spiral and wave images, outperforming several ML and DL models. This approach offers a promising diagnostic tool for early PD detection and provides a foundation for future work to incorporate additional features and enhance detection accuracy.

PMID:40029495 | DOI:10.1007/s11596-025-00017-3

Mikrochim Acta. 2025 Mar 3;192(3):199. doi: 10.1007/s00604-025-06955-y.

ABSTRACT

Graphene-based field-effect transistors (GFETs) are rapidly gaining recognition as powerful tools for biochemical analysis due to their exceptional sensitivity and specificity. In this study, we utilize a GFET system to explore the peroxidase-based biocatalytic behavior of horseradish peroxidase (HRP) and the heme molecule, the latter serving as the core component responsible for HRP's enzymatic activity. Our primary objective is to evaluate the effectiveness of GFETs in analyzing the peroxidase activity of these compounds. We highlight the superior sensitivity of graphene-based FETs in detecting subtle variations in enzyme activity, which is critical for accurate biochemical analysis. Using the transconductance measurement system of GFETs, we investigate the mechanisms of enzymatic reactions, focusing on suicide inactivation in HRP and heme bleaching under two distinct scenarios. In the first scenario, we investigate the inactivation of HRP in the presence of hydrogen peroxide and ascorbic acid as cosubstrate. In the second scenario, we explore the bleaching of the heme molecule under conditions of hydrogen peroxide exposure, without the addition of any cosubstrate. Our findings demonstrate that this advanced technique enables precise monitoring and comprehensive analysis of these enzymatic processes. Additionally, we employed a machine learning algorithm based on a multilayer perceptron deep learning architecture to detect the enzyme parameters under various chemical and environmental conditions. Integrating machine learning and probabilistic methods significantly enhances the accuracy of enzyme behavior predictions.

PMID:40029395 | DOI:10.1007/s00604-025-06955-y

IISE Trans Occup Ergon Hum Factors. 2025 Mar 3:1-14. doi: 10.1080/24725838.2025.2469076. Online ahead of print.

ABSTRACT

OCCUPATIONAL APPLICATIONWe present a computer vision framework that is intended to help enhance workplace safety and productivity across diverse occupational domains by monitoring worker movements and identifying ergonomic risks. By analyzing movement patterns and biomechanics, use of this framework could promote safe and healthy working conditions, helping to prevent injuries and mitigate workplace accidents. Additionally, application of the framework could aid in assessing assistive technologies that support workers with various conditions in completing tasks safely and efficiently, thereby helping to boost productivity.

PMID:40028793 | DOI:10.1080/24725838.2025.2469076

Network. 2025 Mar 3:1-37. doi: 10.1080/0954898X.2024.2447878. Online ahead of print.

ABSTRACT

The proper study and administration of biodiversity relies heavily on accurate plant species identification. To determine a plant's species by manual identification, experts use a series of keys based on measurements of various plant features. The manual procedure, however, is tiresome and lengthy. Recently, advancements in technology have prompted the need for more effective approaches to satisfy species identification standards, such as the creation of digital-image-processing and template tools. There are significant obstacles to fully automating the recognition of plant species, despite the many current research on the topic. In this work, the leaf classification was performed using the ontological relationship between the leaf features and their classes. This relationship was identified by using the swarm intelligence techniques called particle swarm and cuckoo search algorithm. Finally, these features were trained using the traditional machine learning algorithm regression neural network. To increase the effectiveness of the ontology, the machine learning approach results were combined with the deep learning approach called RESNET50 using association rule. The proposed ontology model produced an identification accuracy of 98.8% for GRNN model, 99% accuracy for RESNET model and 99.9% for the combined model for 15 types of medicinal leaf sets.

PMID:40028706 | DOI:10.1080/0954898X.2024.2447878

Heliyon. 2025 Feb 11;11(4):e42641. doi: 10.1016/j.heliyon.2025.e42641. eCollection 2025 Feb 28.

ABSTRACT

Ground-penetrating radar (GPR) is a prominent non-destructive testing (NDT) method for corrosivity evaluation in concrete structures. Most GPR interpretation methods rely solely on the absolute values of rebar reflection intensity, making them vulnerable to misinterpretation of the effects of complex factors. This study introduces a more comprehensive GPR data interpretation method, encompassing analysis in time and time-frequency domains. The developed method constitutes efficient GPR data collection and pre-processing, deep learning rebar recognition, and frequency domain analysis using the Short-Time Fourier Transform (STFT). The center frequency of rebar responses was normalized and depth-corrected to standardize the analysis method. The GPR condition mapping thresholds were optimized and validated using ground truth conditions from hammer tapping and reinforcement exposure of reinforced concrete walls. The method demonstrated superior performance compared to the traditional amplitude-based approach in detecting and quantifying the extent of corrosion-induced deterioration, with an average accuracy of 0.80 for active corrosion and 0.84 for active-corrosion with corrosion-induced delamination.

PMID:40028599 | PMC:PMC11872417 | DOI:10.1016/j.heliyon.2025.e42641

Heliyon. 2025 Feb 8;11(4):e42550. doi: 10.1016/j.heliyon.2025.e42550. eCollection 2025 Feb 28.

ABSTRACT

To classify raw SERS Raman spectra from biological materials, we propose DeepRaman, a new architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation approach. Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. Performance of DeepRaman on 14 endotoxins bacteria and on a public data achieved an extraordinary accuracy of 99 percent. This provides exact endotoxin classification and has tremendous potential for accelerated medical diagnostics and treatment decision-making in cases of pathogenic infections.

BACKGROUND: Bacterial endotoxin, a lipopolysaccharide exuded by bacteria during their growth and infection process, serves as a valuable biomarker for bacterial identification. It is a vital component of the outer membrane layer in Gram-negative bacteria. By employing silver nanorod-based array substrates, surface-enhanced Raman scattering (SERS) spectra were obtained for two separate datasets: Eleven endotoxins produced by bacteria, each having an 8.75 pg average detection quantity per measurement, and three controls chitin, lipoteichoic acid (LTA), bacterial peptidoglycan (PGN), because their structures differ greatly from those of LPS.

OBJECTIVE: This study utilized various classical machine learning techniques, such as support vector machines, k-nearest neighbors, and random forests, in conjunction with a modified deep learning approach called DeepRaman. These algorithms were employed to distinguish and categorize bacterial endotoxins, following appropriate spectral pre-processing, which involved novel filtering techniques and advanced feature extraction methods.

RESULT: Most traditional machine learning algorithms achieved distinction accuracies of over 99 percent, whereas DeepRaman demonstrated an exceptional accuracy of 100 percent. This method offers precise endotoxin classification and holds significant potential for expedited medical diagnoses and therapeutic decision-making in cases of pathogenic infections.

CONCLUSION: We present the effectiveness of DeepRaman, an innovative architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation method, in classifying raw SERS Raman spectral data from biological specimens with unparalleled accuracy relative to conventional machine learning algorithms. Notably, this Convolutional Neural Network (CNN) operates autonomously, requiring no human intervention, and can be applied with substantially smaller datasets than traditional CNNs. Furthermore, it exhibits remarkable proficiency in managing challenging baseline scenarios that often lead to failures in other techniques, thereby promoting the broader clinical adoption of Raman spectroscopy.

PMID:40028585 | PMC:PMC11870271 | DOI:10.1016/j.heliyon.2025.e42550

Heliyon. 2025 Feb 6;11(4):e42525. doi: 10.1016/j.heliyon.2025.e42525. eCollection 2025 Feb 28.

ABSTRACT

Viticulture benefits significantly from rapid grape bunch identification and counting, enhancing yield and quality. Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. On one hand, drone, or Unmanned Aerial Vehicles (UAV) imagery combined with deep learning algorithms has revolutionised agriculture by automating plant health classification, disease identification, and fruit detection. However, these advancements often remain inaccessible to farmers due to their reliance on specialized hardware like ground robots or UAVs. On the other hand, most farmers have access to smartphones. This article proposes a novel approach combining UAVs and smartphone technologies. An AI-based framework is introduced, integrating a 5-stage AI pipeline combining object detection and pixel-level segmentation algorithms to automatically detect grape bunches in smartphone images of a commercial vineyard with vertical trellis training. By leveraging UAV-captured data for training, the proposed model not only accelerates the detection process but also enhances the accuracy and adaptability of grape bunch detection across different devices, surpassing the efficiency of traditional and purely UAV-based methods. To this end, using a dataset of UAV videos recorded during early growth stages in July (BBCH77-BBCH79), the X-Decoder segments vegetation in the front of the frames from their background and surroundings. X-Decoder is particularly advantageous because it can be seamlessly integrated into the AI pipeline without requiring changes to how data is captured, making it more versatile than other methods. Then, YOLO is trained using the videos and further applied to images taken by farmers with common smartphones (Xiaomi Poco X3 Pro and iPhone X). In addition, a web app was developed to connect the system with mobile technology easily. The proposed approach achieved a precision of 0.92 and recall of 0.735, with an F1 score of 0.82 and an Average Precision (AP) of 0.802 under different operation conditions, indicating high accuracy and reliability in detecting grape bunches. In addition, the AI-detected grape bunches were compared with the actual ground truth, achieving an R2 value as high as 0.84, showing the robustness of the system. This study highlights the potential of using smartphone imaging and web applications together, making an effort to integrate these models into a real platform for farmers, offering a practical, affordable, accessible, and scalable solution. While smartphone-based image collection for model training is labour-intensive and costly, incorporating UAV data accelerates the process, facilitating the creation of models that generalise across diverse data sources and platforms. This blend of UAV efficiency and smartphone precision significantly cuts vineyard monitoring time and effort.

PMID:40028582 | PMC:PMC11869025 | DOI:10.1016/j.heliyon.2025.e42525

Heliyon. 2025 Feb 3;11(4):e42428. doi: 10.1016/j.heliyon.2025.e42428. eCollection 2025 Feb 28.

ABSTRACT

With pedestrian crossings implicated in a significant proportion of vehicle-pedestrian accidents and the French government's initiatives to improve pedestrian safety, there is a pressing need for efficient, large-scale evaluation of pedestrian crossings. This study proposes the deployment of advanced deep learning neural networks to automate the assessment of pedestrian crossings and roundabouts, leveraging aerial and street-level imagery sourced from Google Maps and Google Street View. Utilizing ConvNextV2, ResNet50, and ResNext50 models, we conducted a comprehensive analysis of pedestrian crossings across various urban and rural settings in France, focusing on nine identified risk factors. Our methodology incorporates Mask R-CNN for precise segmentation and detection of zebra crossings and roundabouts, overcoming traditional data annotation challenges and extending coverage to underrepresented areas. The analysis reveals that the ConvNextV2 model, in particular, demonstrates superior performance across most tasks, despite challenges such as data imbalance and the complex nature of variables like visibility and parking proximity. The findings highlight the potential of convolutional neural networks in improving pedestrian safety by enabling scalable and objective evaluations of crossings. The study underscores the necessity for continued dataset augmentation and methodological advancements to tackle identified challenges. Our research contributes to the broader field of road safety by demonstrating the feasibility and effectiveness of automated, image-based pedestrian crossing audits, paving the way for more informed and effective safety interventions.

PMID:40028551 | PMC:PMC11872108 | DOI:10.1016/j.heliyon.2025.e42428

Clin Transl Radiat Oncol. 2025 Feb 11;52:100933. doi: 10.1016/j.ctro.2025.100933. eCollection 2025 May.

ABSTRACT

INTRODUCTION: The integration of artificial intelligence into radiotherapy planning for prostate cancer has demonstrated promise in enhancing efficiency and consistency. In this study, we assess the clinical feasibility of a fully automated machine learning (ML)-based "one-click" workflow that combines ML-based segmentation and treatment planning. The proposed workflow was designed to create a clinically acceptable radiotherapy plan within the inter-observer variation of conventional plans.

METHODS: We evaluated the fully-automated workflow on five low-risk prostate cancer patients treated with external beam radiotherapy and compared the results with conventional optimized and inverse planned radiotherapy plans based on the contours of six different experienced radiation oncologists. Both qualitative and quantitative metrics were analyzed. Additionally, we evaluated the dose distribution of the ML-based and conventional radiation treatment plans on the different segmentations (manual vs. manual and manual vs. automation).

RESULTS: The automatic deep-learning segmentation of the target volume revealed a close agreement between the deep-learning based and expert contours referring to Dice Similarity- and Hausdorff index. However, the deep-learning based CTVs had a significantly smaller volume than the expert CTVs (47.1 cm3 vs. 62.6 cm3). The fully automated ML-based plans provide clinically acceptable dose coverage within the range of inter-observer variability observed in the manual plans. Due to the smaller segmentation of the CTV the dose coverage of the CTV and PTV (expert contours) were significantly lower than that of the manual plans.

CONCLUSION: Our study indicates that the tested fully automated ML-based workflow is clinically feasible and leads to comparable results to conventional radiation treatment plans. This represents a promising step towards efficient and standardized prostate cancer treatment. Nevertheless, in the evaluated cohort, auto segmentation was associated with smaller target volumes compared to manual contours, highlighting the necessity of improving segmentation models and prospective testing of automation in radiation therapy.

PMID:40028424 | PMC:PMC11871478 | DOI:10.1016/j.ctro.2025.100933

ACS Cent Sci. 2024 Aug 30;11(2):219-227. doi: 10.1021/acscentsci.4c00656. eCollection 2025 Feb 26.

ABSTRACT

Innovative approaches to design molecules with tailored properties are required in various research areas. Deep learning methods can accelerate the discovery of new materials by leveraging molecular structure-property relationships. In this study, we successfully developed a generative deep learning (Gen-DL) model that was trained on a large experimental database (DBexp) including 71,424 molecule/solvent pairs and was able to design molecules with target properties in various solvents. The Gen-DL model can generate molecules with specified optical properties, such as electronic absorption/emission peak position and bandwidth, extinction coefficient, photoluminescence (PL) quantum yield, and PL lifetime. The Gen-DL model was shown to leverage the essential design principles of conjugation effects, Stokes shifts, and solvent effects when it generated molecules with target optical properties. Additionally, the Gen-DL model was demonstrated to generate practically useful molecules developed for real-world applications. Accordingly, the Gen-DL model can be a promising tool for the discovery and design of novel molecules with tailored properties in various research areas, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic photodiodes (OPDs), bioimaging dyes, and so on.

PMID:40028364 | PMC:PMC11869130 | DOI:10.1021/acscentsci.4c00656

bioRxiv [Preprint]. 2025 Feb 17:2025.02.12.637970. doi: 10.1101/2025.02.12.637970.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable lung disease characterized by tissue scarring that disrupts gas exchange. Epithelial cell dysfunction, fibroblast activation, and excessive extracellular matrix deposition drive this pathology that ultimately leads to respiratory failure. Mechanistic studies have shown that repeated injury to alveolar epithelial cells initiates an aberrant wound-healing response in surrounding fibroblasts through secretion of mediators like transforming growth factor-β, yet the precise biological pathways contributing to disease progression are not fully understood. To better study these interactions there is a critical need for lung models that replicate the cellular heterogeneity, geometry, and biomechanics of the distal lung microenvironment. In this study, induced pluripotent stem cell-derived alveolar epithelial type II (iATII) cells and human pulmonary fibroblasts were arranged to replicate human lung micro-architecture and embedded in soft or stiff poly(ethylene glycol) norbornene (PEG-NB) hydrogels that recapitulated the mechanical properties of healthy and fibrotic lung tissue, respectively. The co-cultured cells were then exposed to pro-fibrotic biochemical cues, including inflammatory cytokines and growth factors. iATIIs and fibroblasts exhibited differentiation pathways and gene expression patterns consistent with trends observed during IPF progression in vivo . A design of experiments statistical analysis identified stiff hydrogels combined with pro-fibrotic biochemical cue exposure as the most effective condition for modeling fibrosis in vitro . Finally, treatment with Nintedanib, one of only two Food and Drug Administration (FDA)-approved drugs for IPF, was assessed. Treatment reduced fibroblast activation, as indicated by downregulation of key activation genes, and upregulated several epithelial genes. These findings demonstrate that human 3D co-culture models hold tremendous potential for advancing our understanding of IPF and identifying novel therapeutic targets.

STATEMENT OF SIGNIFICANCE: This study leverages advanced biomaterials and biofabrication techniques to engineer physiologically relevant, patient-specific, and sex-matched models of pulmonary fibrosis, addressing the critical need for pre-clinical therapeutic drug screening platforms. These human 3D lung models successfully replicated key features of fibrotic lung tissue. Tuning microenvironmental stiffness of 3D PEG-NB hydrogels to match fibrotic lung values and exposing human iATII cells and fibroblasts to pro-inflammatory biochemical cues recreated hallmark characteristics of in vivo fibrosis pathogenesis, including epithelial differentiation and loss, as well as fibroblast activation. The utility of these models was further validated by demonstrating responsiveness to Nintedanib, a clinically available treatment for IPF. These findings highlight the transformative potential of well-defined biomaterial-based 3D models for elucidating complex disease mechanisms and accelerating therapeutic drug discovery for chronic pulmonary diseases like idiopathic pulmonary fibrosis.

PMID:40027659 | PMC:PMC11870410 | DOI:10.1101/2025.02.12.637970

Methods Mol Biol. 2025;2909:165-178. doi: 10.1007/978-1-0716-4442-3_12.

ABSTRACT

Combining mathematical modeling with experiments enables quantitative understanding of cell signaling, transcriptional regulation, and cell fate decisions. Here, we provide a systems biology approach to link signal transduction with B cells fate decisions, to enable quantitative prediction of B-cell proliferation, and differentiation. We describe methodology to run simulations that reveal how signal transduction regulates gene expression and predicts cell fate decision. We describe how to quantitively validate modeling predictions with wet-lab experiments.

PMID:40029522 | DOI:10.1007/978-1-0716-4442-3_12

Microbiol Spectr. 2025 Mar 3:e0212924. doi: 10.1128/spectrum.02129-24. Online ahead of print.

ABSTRACT

Micromonospora strains proved to be a model organism for drug discovery and plant growth promotion (PGP). Strain DSM 115977 T was subjected to polyphasic taxonomic analysis and genome mining for biosynthetic gene clusters and PGP-associated genes in order to determine its taxonomic rank and assess its biosynthetic potential. The strain was found to form a novel species within the evolutionary radiation of the genus Micromonospora. The strain contained glucose, mannose, xylose, and ribose as whole-cell sugars and the isomer DL-diaminopimelic acid in its peptidoglycan. Strain DSM 115977T had iso-C15:0, iso-C16:0, C17:1cis 9, C17:0, iso-C17:0, and 10-methyl-C17:0 as fatty acid profile (>5%) and MK10-H4 and MK10-H6 as the predominant menaquinones (>10%). The polar lipid profile consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol, glycophosphatidylinositol, glycophospholipids, phosphoaminolipid, unidentified lipids, and phospholipids. The genome of the strain had a size of 7.0 Mbp with a DNA G + C content of 73.4%. It formed a well-supported sub-clade with its close phylogenomic neighbor, Micromonospora echinofusca DSM 43913T (98.7%). Digital DNA-DNA hybridization and average nucleotide identity derived from sequence comparisons between the strain and its close phylogenomic neighbors were below the thresholds of 70 and 95-96% for prokaryotic species demarcation, respectively. Based on these findings, strain DSM 115977T (Asg4T = KCTC 59188T) merits to be considered as the type strain of a new species for which the name Micromonospora reichwaldensis sp. nov. is proposed. Genome mining for biosynthetic gene clusters encoding specialized secondary metabolites highlighted its ability to produce potentially novel therapeutic compounds. The strain is rich in plant growth-promoting genes whose predicted products directly and indirectly affect the development and immune system of the plant.

IMPORTANCE: In view of the significant pharmaceutical, biotechnological, and ecological potentials of micromonosporae, it is particularly interesting to enhance the genetic diversity of this genus by focusing on the isolation of novel strain from underexplored habitats, with the promise that novel bacteria will lead to new chemical entities. In this report, modern polyphasic taxonomic study confirmed the assignment of strain DSM 115977T to a novel species for which the name Micromonospora reichwaldensis sp. nov. is proposed. The strain harbors in its genomic sequence several biosynthetic gene clusters for secondary metabolites and genes associated with plant growth-promoting features. The results of this study provide a very useful basis for launching more in-depth research into agriculture and/or drug discovery.

PMID:40029309 | DOI:10.1128/spectrum.02129-24

Plant J. 2025 Mar;121(5):e70047. doi: 10.1111/tpj.70047.

ABSTRACT

Plants are essential for human survival. Over the past three decades, work with the reference plant Arabidopsis thaliana has significantly advanced plant biology research. One key event was the sequencing of its genome 25 years ago, which fostered many subsequent research technologies and datasets. Arabidopsis has been instrumental in elucidating plant-specific aspects of biology, developing research tools, and translating findings to crop improvement. It not only serves as a model for understanding plant biology and but also biology in other fields, with discoveries in Arabidopsis also having led to applications in human health, including insights into immunity, protein degradation, and circadian rhythms. Arabidopsis research has also fostered the development of tools useful for the wider biological research community, such as optogenetic systems and auxin-based degrons. This 4th Multinational Arabidopsis Steering Committee Roadmap outlines future directions, with emphasis on computational approaches, research support, translation to crops, conference accessibility, coordinated research efforts, climate change mitigation, sustainable production, and fundamental research. Arabidopsis will remain a nexus for discovery, innovation, and application, driving advances in both plant and human biology to the year 2030, and beyond.

PMID:40028766 | DOI:10.1111/tpj.70047

F1000Res. 2024 Sep 13;13:1055. doi: 10.12688/f1000research.155928.1. eCollection 2024.

ABSTRACT

CSNK1A1 is a key regulator of various signalling pathways, including the Wnt/β-catenin pathway. Playing a central role in cellular function and disease pathology, CSNK1A1 has emerged as an attractive protein target for therapeutic development. In this study we characterize ten CSNK1A1 commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. This study is part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While the use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:40028451 | PMC:PMC11868743 | DOI:10.12688/f1000research.155928.1

F1000Res. 2025 Feb 20;13:886. doi: 10.12688/f1000research.154058.2. eCollection 2024.

ABSTRACT

BACKGROUND: The mass vaccination campaign against COVID-19 has been commonly considered the best response to the global COVID-19 pandemic crisis. However, assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status. The UK is perhaps the only country which has made publicly available all-cause mortality data by vaccination status.

METHODS: Data from April 2021 to May 2023 published by the UK Office for National Statistics (ONS) were retrospectively analyzed by age groups and vaccination status; the standardized mortality ratio (SMR) for all-cause and non-COVID-19 mortality was calculated against the corresponding unvaccinated groups.

RESULTS: We found that across all age groups, all-cause mortality SMRs increased from a certain date, dependent on the age group. Across all age groups, all-cause mortality SMRs were initially much lower than 1. However, due to their increase, by a certain date for the 18-39, 80-89 and 90+ age groups they exceeded the reference value. For the other age groups, the date at which the SMR would reach 1 can be predicted, provided the trend is maintained. Non-COVID-19 SMRs' trends were very similar. Their initial values much lower than 1 are suggestive of significant biases in the ONS dataset, leading to underestimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths.

CONCLUSIONS: The increase over time in all-cause death SMRs in vaccinated people compared to unvaccinated, and their excess from the reference values for certain age groups, should be carefully considered to understand the underlying factors. Furthermore, since the initial values of the SMRs are much lower than 1, we assume the presence of significant biases in the ONS dataset, leading to understimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths. It would be desirable for other major countries to systematically collect all-cause mortality by vaccination status and, in the meantime, a pending indepth investigations, much greater caution should be exercised in promoting mass vaccination campaigns.

PMID:40028449 | PMC:PMC11868741 | DOI:10.12688/f1000research.154058.2