Int Forum Allergy Rhinol. 2025 Jan 15. doi: 10.1002/alr.23533. Online ahead of print.

NO ABSTRACT

PMID:39811887 | DOI:10.1002/alr.23533

Biofilm. 2024 Dec 20;9:100246. doi: 10.1016/j.bioflm.2024.100246. eCollection 2025 Jun.

ABSTRACT

Biofilm infections are chronic infections which are difficult to diagnose. Biofilm infections are tolerant to antibiotics and the defense mechanisms of the host. Patients with the genetic disease cystic fibrosis (CF) produce viscid mucus in the respiratory tract and therefore suffer from chronic biofilm infections in their lungs and paranasal sinuses. The most important microorganism is the mucoid phenotype of Pseudomonas aeruginosa which causes chronic biofilm infections in the lungs of CF patients and untreated patients succumb as children if they contact this biofilm infection. Since CF patients are treated in CF Centers all over the world, it is possible to do longitudinal studies on epidemiology, pathophysiology, diagnosis, prevention and treatment of P. aeruginosa biofilm infection which is not possible if such patients are not followed in specialized centers. This survey describes the research through several decades in the Danish CF Center in Copenhagen which have changed the epidemiology, treatment, prophylaxis and prognosis of CF patients worldwide. Based on these results ESCMID Guidelines for diagnosis and treatment of biofilm infections were published which have influenced biofilm research and treatment in other areas.

PMID:39811797 | PMC:PMC11732244 | DOI:10.1016/j.bioflm.2024.100246

ERJ Open Res. 2025 Jan 13;11(1):00137-2024. doi: 10.1183/23120541.00137-2024. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: Chronic rhinosinusitis (CRS) and olfactory dysfunction (OD) are prevalent disease complications in people with cystic fibrosis. These understudied comorbidities significantly impact quality of life. The impact of highly effective modulator therapy (HEMT) in young children with cystic fibrosis (YCwCF) on these disease complications is unknown. This proposed study aims to characterise CRS and OD in YCwCF and assess the efficacy of HEMT in improving sinus and olfactory health in this young age group.

METHODS: This six-centre, prospective, observational study will enrol 80 YCwCF aged 2-8 years. Patients are divided into two groups: those receiving HEMT and those not on HEMT based on clinical indication. Both groups undergo sinus magnetic resonance imaging, psychophysical olfactory tests, and complete patient- or parent-reported quality of life surveys over 2 years. Outcomes will be compared before and after initiation of HEMT and between groups. Ethical approval has been obtained for all sites, and this study has been registered on ClinicalTrials.gov (NCT06191640).

RESULTS: Enrolment began in April 2023. 21 participants have been enrolled as of October 2023 with ongoing enrolment at all sites.

CONCLUSION: This investigation is expected to provide critical insights into the potential benefits of early HEMT initiation in managing CRS and OD in YCwCF. It will assist in developing targeted interventions and contribute to the understanding of HEMT's role in altering the disease course in this demographic.

PMID:39811548 | PMC:PMC11726580 | DOI:10.1183/23120541.00137-2024

ERJ Open Res. 2025 Jan 13;11(1):00587-2024. doi: 10.1183/23120541.00587-2024. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: People with cystic fibrosis (CF) variants that exhibit residual function (RF) of the CF transmembrane conductance regulator are considered to have a milder disease; however, the spectrum of CF phenotype within the different RF variants has not been extensively investigated. The aim of the present study was to characterise the spectrum of CF disease severity in people with CF (pwCF) carrying different RF variants, using the European Cystic Fibrosis Society Patient Registry (ECFSPR) data.

METHODS: A retrospective cross-sectional and longitudinal cohort study included data from the ECFSPR during 2008-2016. Demographic and clinical characteristics of pwCF carrying different RF variants were compared with the characteristics of pwCF who are homozygous for F508del. Among those with RF, a distinction was made between pwCF carrying class IV or class V variants and pwCF carrying specific RF variants.

RESULTS: Out of 56 701 pwCF in the ECFSPR, 6192 carried RF variants and 22 766 were homozygous for F508del. Class IV/F508del variants were associated with a milder course than class V/F508del; both were milder than pwCF homozygous for F508del. Forced expiratory volume in 1 s % predicted (FEV1pp) declined in childhood in all groups. For adults, the hazard ratio of death for class V/F508del versus class IV/F508del was 2.14 (95% confidence interval 0.99-4.63, p=0.052). PwCF carrying 3849+10 kb C→T/F508del and pwCF carrying R334W/F508del had age-specific FEV1pp and chronic bacterial colonisation similar to those of pwCF homozygous for F508del.

CONCLUSION: There is a wide spectrum of disease severity between the different RF variants. Some, such as those carrying 3849+10 kb C→T, have severe disease, similar to that of pwCF homozygous for F508del.

PMID:39811546 | PMC:PMC11726569 | DOI:10.1183/23120541.00587-2024

IDCases. 2024 Dec 15;39:e02132. doi: 10.1016/j.idcr.2024.e02132. eCollection 2025.

ABSTRACT

Mycobacterium Chelonae is a rapidly growing nontuberculous mycobacterium (NTM) that is ubiquitous in the environment and is associated with skin and soft tissue infections (1). Because Mycobacterium Chelonae is an opportunistic infection, it can present as skin abscess, cellulitis, osteomyelitis, pulmonary infection or disseminated infections, particularly in individuals with compromised immune systems or underlying lung conditions such as cystic fibrosis or bronchiectasis. M.Chelonae is one of the most pathogenic rapidly growing mycobacteria (RGM). Diagnosing RGM and distinguishing it from Mycobacterium tuberculosis is important because public health tracking and management is different in these two organisms. Antibiotic susceptibility testing can also provide valuable clues to the species identification of RGM as each species has a specific in vitro antibiotic susceptibility pattern (2). Although incidence of M. Chelonae is increasing, these infections often remain misdiagnosed. This case report discusses the clinical presentation, diagnostic challenges, the rationale for early empiric treatment, and therapeutic options for M. Chelonae infection, emphasizing the importance of timely intervention in immunocompromised individuals.

PMID:39810811 | PMC:PMC11732071 | DOI:10.1016/j.idcr.2024.e02132

Cancer Med. 2025 Jan;14(2):e70574. doi: 10.1002/cam4.70574.

ABSTRACT

BACKGROUND: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for advanced image processing technology such as Deep Learning (DL).

AIM: Our review focused on the use of DL in multidisciplinary imaging in pediatric surgical oncology.

METHODS: A search was conducted within three databases (Pubmed, Embase, and Scopus), and 2056 articles were identified. Three separate screenings were performed for each identified subfield.

RESULTS: In total, we identified 36 articles, divided between radiology (n = 22), pathology (n = 9), and other image-based diagnostics (n = 5). Four types of tasks were identified in our review: classification, prediction, segmentation, and synthesis. General statements about the studies'' performance could not be made due to the inhomogeneity of the included studies. To implement DL in pediatric clinical practice, both technical validation and clinical validation are of uttermost importance.

CONCLUSION: In conclusion, our review provided an overview of all DL research in the field of pediatric surgical oncology. The more advanced status of DL in adults should be used as guide to move the field of DL in pediatric oncology further, to keep improving the outcomes of children with cancer.

PMID:39812075 | DOI:10.1002/cam4.70574

J Educ Health Promot. 2024 Nov 29;13:456. doi: 10.4103/jehp.jehp_141_24. eCollection 2024.

ABSTRACT

BACKGROUND: Reflection is one of the main components of the medical sciences curriculum. It is one of the learner-centered educational strategies, leading to deep learning, and is necessary to attain professional capabilities. A pertinent challenge is how to assess reflection. This study was conducted to design and assess psychometric characteristics of medical sciences students' reflection skills self-assessment questionnaire (RSSAQ) in Persian.

MATERIALS AND METHODS: This is a methodological explorative study conducted at our University of Medical Sciences. First, an item pool was collected from both the literature review (previously designed questionnaires and existent models of reflection) and experts' and researchers' perspectives. Then the initial version of the questionnaire was presented to 19 experts and 50 students to assess the face and content validity. To assess the reliability, 48 students filled out the questionnaire twice at a one-week interval. To assess the construct validity, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were done. For doing so, 151 students filled out the questionnaire. The data was analyzed using IBM SPSS (Statistical Package for the Social Sciences) Statistics for Windows version 16 and Analysis of Moment Structures (AMOS).

RESULTS: The content validity index (CVI), content validity ratio (CVR), and impact score (IS) for the questionnaire came out to be 0.91, 0.75, and 4.68, respectively. Regarding the reliability, Cronbach's alpha and intraclass correlation coefficient (ICC) were, respectively, 0.75 and 0.8 (with a 95 percent confidence interval). Regarding the construct validity, three factors were extracted, labeled as "readiness to reflect" (RTR), "reflection in action" (RIA), and "reflection on action" (ROA). It became clear that this questionnaire can predict 36.8 percent of variations in reflective behavior or process in students. CFA determined that there is a positive and significant correlation between RIA and ROA factors. However, the RTR factor has a negative correlation with the other factors and vice versa.

CONCLUSION: The questionnaire designed in this study for reflection self-assessment had acceptable psychometric characteristics and can be applied in curriculum planning, educational evaluations, and designing educational interventions.

PMID:39811846 | PMC:PMC11731446 | DOI:10.4103/jehp.jehp_141_24

Alzheimers Dement (Amst). 2025 Jan 14;17(1):e70041. doi: 10.1002/dad2.70041. eCollection 2025 Jan-Mar.

ABSTRACT

INTRODUCTION: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI.

METHODS: We performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants. Features from OCT were used to develop an ensemble trilateral deep-learning model.

RESULTS: The trilateral model significantly outperformed single non-deep learning models in Asian (area under the curve [AUC] = 0.91 vs. 0.71-0.72, p = 0.022-0.032) and White (AUC = 0.84 vs. 0.58-0.75, p = 0.056- < 0.001) populations. However, its performance was comparable to that of the trilateral statistical model (AUCs similar, p > 0.05).

DISCUSSION: Both multimodal approaches, using deep learning or traditional statistical models, show promise for AD and MCI detection. The choice between these models may depend on computational resources, interpretability preferences, and clinical needs.

HIGHLIGHTS: A deep-learning algorithm was developed to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) using OCT images.The combined model outperformed single OCT parameters in both Asian and White cohorts.The study demonstrates the potential of OCT-based deep-learning algorithms for AD and MCI detection.

PMID:39811700 | PMC:PMC11730192 | DOI:10.1002/dad2.70041

Osteoarthr Cartil Open. 2024 Nov 30;7(1):100551. doi: 10.1016/j.ocarto.2024.100551. eCollection 2025 Mar.

ABSTRACT

OBJECTIVE: Lower limb malalignment can complicate symptoms and accelerate knee osteoarthritis (OA), necessitating consideration in study population selection. In this study, we develop and validate a deep learning model that classifies leg alignment as "normal" or "malaligned" from knee antero-posterior (AP)/postero-anterior (PA) radiographs alone, using an adjustable hip-knee-ankle (HKA) angle threshold.

MATERIAL AND METHODS: We utilized 8878 digital radiographs, including 6181 AP/PA full-leg x-rays (LLRs) and 2697 AP/PA knee x-rays (2292 with positioning frame, 405 without). The model's evaluation involved two steps: In step 1, the model's predictions on knee images cropped from LLRs were compared against the ground truth from the original LLRs. In step 2, the model was tested on knee AP radiographs, using corresponding same-day LLRs as a proxy for ground truth.

RESULTS: The model effectively classified alignment, with step one achieving sensitivity and specificity of 0.92 for a threshold of 7.5°, and 0.90 and 0.85 for 5°. For positioning frame images, step two showed a sensitivity of 0.85 and specificity of 0.81 for 7.5°, and 0.79 and 0.74 for 5°. For non-positioning frame images, sensitivity and specificity were 0.91 and 0.83 for 7.5°, and 0.9 and 0.86 for 5°.

CONCLUSION: The model developed in this study accurately classifies lower limb malalignment from AP/PA knee radiographs using adjustable thresholds, offering a practical alternative to LLRs. This can enhance the precision of study population selection and patient management.

PMID:39811691 | PMC:PMC11729668 | DOI:10.1016/j.ocarto.2024.100551

Data Brief. 2024 Dec 11;58:111221. doi: 10.1016/j.dib.2024.111221. eCollection 2025 Feb.

ABSTRACT

Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data. This enriched dataset, organized in the KITTI 3D object detection benchmark format by the Karlsruhe Institute of Technology and the Toyota Technological Institute, improves its utility for training and evaluating autonomous vehicle systems. To bridge the gap between simulated and real-world scenarios, our methodology integrates a wide range of scenarios simulation in CARLA, including variations in weather conditions, human presence, and different environmental settings. This approach enhances the realism and robustness of the dataset, making it more applicable to practical autonomous driving scenarios. The data provided in this article offers a valuable resource for researchers, industry professionals, and stakeholders interested in advancing autonomous vehicle technologies and improving emergency vehicle detection. Furthermore, this dataset contributes to broader efforts in road safety and the development of AI systems capable of handling specialized vehicle identification in real-world applications.

PMID:39811523 | PMC:PMC11730950 | DOI:10.1016/j.dib.2024.111221

Data Brief. 2024 Dec 19;58:111244. doi: 10.1016/j.dib.2024.111244. eCollection 2025 Feb.

ABSTRACT

A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations. Each of these images was standardized to 800 × 800 pixels resolution, so that consistency may be maintained among the dataset. All images were labelled in the nine prefixed categories: anthracnose, bacterial blight, citrus canker, curl virus, deficiency leaf, dry leaf, healthy leaf, sooty mould, and spider mites. In the present study, a DenseNet-121 architecture was used, where 20 % of the dataset was kept for validation and the remaining 80 % for training. A trained model with a batch size of 32 was trained for 30 epochs, achieving an accuracy of 98.56 % with augmentation, and 96.19 % without it. The dataset will not only act as a benchmark in developing accurate machine learning models for early disease detection, but it will also contribute to the cause of sustainable lemon cultivation practices by facilitating timely and effective disease management interventions.

PMID:39811522 | PMC:PMC11732584 | DOI:10.1016/j.dib.2024.111244

Data Brief. 2024 Dec 10;58:111216. doi: 10.1016/j.dib.2024.111216. eCollection 2025 Feb.

ABSTRACT

Epipremnum aureum, sometimes known as the Money Plant, is a popular houseplant known for its hearts-shaped leaves and durability. Commonly referred to as Golden Pothos or Devil's Ivy, it is also appreciated for its ornamental value and air cleaning ability. They say that these plants are attractive to many people owing to their tolerance to several conditions and easy care, therefore, it is no surprise that they are found in many households and workplaces. Money Plants are hardy, but like any other plant they can also be infected by various diseases, which may render them less attractive, or even unattractive. This work encompasses bacterial wilt, manganese poisoning aspects and together with a healthy leaves aspect presents all prevalent masses and offer a comprehensive image of diseases. A dataset of 224 × 224 pixel images is utilized to accomplish this work with the intention to further enhance support in Ornamental Horticulture practices and diagnose more accurately. This work not only contributes ideas and approaches in understanding the field of plants pathology but also stresses on the fact how image processing can be beneficial in looking after plants. The dataset serves as a solid foundation for deep learning approaches into Ornamental Agriculture and provides useful insights for researchers studying the cultivation of money plants.

PMID:39811518 | PMC:PMC11729688 | DOI:10.1016/j.dib.2024.111216

Biol Methods Protoc. 2025 Jan 6;10(1):bpae097. doi: 10.1093/biomethods/bpae097. eCollection 2025.

ABSTRACT

A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference. Specifically, motivated by the clustered distribution of known RNA structures, a collection of distinct DL models is trained by iteratively leaving one cluster out. Each DL model hence serves as an expert on all but one cluster in the training data. Consequently, for an ID sequence, all but one DL model makes accurate predictions consistent with one another, while an OOD sequence yields highly inconsistent predictions among all DL models. Through consensus analysis of DL predictions, test sequences are categorized as ID or OOD. ID sequences are subsequently predicted by averaging the DL models in consensus, and OOD sequences are predicted using physics-based models. Instead of remediating generalization gaps with alternative approaches such as transfer learning and sequence alignment, MoEFold2D circumvents unpredictable ID-OOD gaps and combines the strengths of DL and physics-based models to achieve accurate ID and robust OOD predictions.

PMID:39811444 | PMC:PMC11729747 | DOI:10.1093/biomethods/bpae097

Heliyon. 2024 Dec 16;11(1):e40855. doi: 10.1016/j.heliyon.2024.e40855. eCollection 2025 Jan 15.

ABSTRACT

INTRODUCTION: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards.

METHODS: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols.

RESULTS: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met.

CONCLUSION: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities and mitigates the risk of personnel becoming infection vectors.

PMID:39811271 | PMC:PMC11730239 | DOI:10.1016/j.heliyon.2024.e40855

Ophthalmol Sci. 2024 Nov 12;5(2):100653. doi: 10.1016/j.xops.2024.100653. eCollection 2025 Mar-Apr.

ABSTRACT

PURPOSE: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.

DESIGN: Retrospective observational study.

PARTICIPANTS: Corneal scrapings from 568 patients with suspected fungal keratitis; 51% contained filamentous fungi according to human expert interpretation.

METHODS: Dual stream multiple instance learning was employed to analyze WSI of KOH smears. Due to the extensive size of these images, often exceeding 100 000 pixels, conventional computer vision methods (e.g., convolutional neural networks) are not feasible. Dual stream multiple instance learning segments the WSI into patches for analysis, extracting relevant features from each patch and aggregating these to make a comprehensive slide-level diagnosis while generating heat maps to visualize areas contributing most to the prediction. Fivefold cross-validation was used for training and validation, with a hold-out test set comprising 15% of the total samples.

MAIN OUTCOME MEASURES: Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F1 score, positive predictive value (PPV), and negative predictive value (NPV) in distinguishing fungal from nonfungal slides.

RESULTS: Dual stream multiple instance learning demonstrated an overall AUC of 0.88 with an accuracy of 79% and an F1 score of 0.79 in distinguishing fungal from nonfungal slides, with sensitivity of 85%, specificity of 71%, PPV of 80%, and NPV of 79%. For "consensus cases," where 2 human graders agreed on the slide interpretation, the model achieved an accuracy of 85% and an F1 score of 0.85. For "discrepant cases," the accuracy was 71% with an F1 score of 0.71. The generated heatmaps highlighted regions corresponding to fungal elements. Code and models are open-sourced and available at https://github.com/Redd-Cornea-AI/KOH-Smear-DSMIL.

CONCLUSIONS: The DSMIL framework shows significant promise in automating interpretation of KOH smears. Its capability to handle large, high-resolution WSI data and accurately detect fungal infections, while providing visual explanations through heatmaps, could enhance the scalability of KOH smear interpretation, ultimately reducing the global burden of blindness from infectious keratitis.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:39811263 | PMC:PMC11731208 | DOI:10.1016/j.xops.2024.100653

iScience. 2024 Dec 10;28(1):111569. doi: 10.1016/j.isci.2024.111569. eCollection 2025 Jan 17.

ABSTRACT

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy requires therapeutic combinations that induce quality T cells. Tumor microenvironment (TME) analysis following therapeutic interventions can identify response mechanisms, informing design of effective combinations. We provide a reference single-cell dataset from tumor-infiltrating leukocytes (TILs) from a human neoadjuvant clinical trial comparing the granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting allogeneic PDAC vaccine GVAX alone, in combination with anti-PD1 or with both anti-PD1 and CD137 agonist. Treatment with GVAX and anti-PD-1 led to increased CD8+ T cell activation and expression of cytoskeletal and extracellular matrix (ECM)-interacting components. Addition of CD137 agonist increased abundance of clonally expanded CD8+ T cells and increased immunosuppressive TREM2 signaling in tumor associated macrophages (TAMs), identified by comparison of ligand-receptor networks, corresponding to changes in metabolism and ECM interactions. These findings associate therapy with GVAX, anti-PD1, and CD137 agonist with enhanced CD8+ T cell function while inducing alternative immunosuppressive pathways in patients with PDAC.

PMID:39811671 | PMC:PMC11730579 | DOI:10.1016/j.isci.2024.111569

iScience. 2024 Dec 9;28(1):111521. doi: 10.1016/j.isci.2024.111521. eCollection 2025 Jan 17.

ABSTRACT

Cellular fitness depends on multiple phenotypes that must be balanced during evolutionary adaptation. For instance, coordinating growth and motility is critical for microbial colonization and cancer invasiveness. In bacteria, these phenotypes are controlled by local regulators that target single operons, as well as by global regulators that impact hundreds of genes. However, how the different levels of regulation interact during evolution is unclear. Here, we measured in Escherichia coli how CRISPR-mediated knockdowns of global and local transcription factors impact growth and motility in three environments. We found that local regulators mostly modulate motility, whereas global regulators jointly modulate growth and motility. Simulated evolutionary trajectories indicate that local regulators are typically altered first to improve motility before global regulators adjust growth and motility following their trade-off. These findings highlight the role of pleiotropic regulators in the adaptation of multiple phenotypes.

PMID:39811663 | PMC:PMC11731283 | DOI:10.1016/j.isci.2024.111521

iScience. 2024 Dec 9;28(1):111549. doi: 10.1016/j.isci.2024.111549. eCollection 2025 Jan 17.

ABSTRACT

Glial-vascular interactions are critical for the formation and maintenance of brain blood vessels and the blood-brain barrier (BBB) in mammals, but their role in the zebrafish BBB remains unclear. Using three glial gene promoters-gfap, glast, and glastini (a truncated glast)-we explored glial-vascular development in zebrafish. Sparse labeling showed fewer glial-vascular interactions at early stages, with glial coverage and contact area increasing with age. Stable transgenic lines for glast and glastini revealed similar developmental increases, starting at ∼30% coverage at 3 days post-fertilization (dpf) and peaking at ∼60% by 10 dpf, and consistently higher glial coverage in the forebrain and midbrain than in the hindbrain. Electron microscopy analyses showed similar progressive increases in glial-vascular interactions, with maximal coverage of ∼70% in adults-significantly lower than the ∼100% seen in mammals. These findings define the temporal and regional maturation of glial-vascular interactions in zebrafish and highlight differences from mammalian systems.

PMID:39811646 | PMC:PMC11731618 | DOI:10.1016/j.isci.2024.111549

ERJ Open Res. 2025 Jan 13;11(1):00486-2024. doi: 10.1183/23120541.00486-2024. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: Pulmonary arterial hypertension (PAH) is a deadly disease without effective non-invasive diagnostic and prognostic testing. It remains unclear whether vasodilators reverse inflammatory activation, a part of PAH pathogenesis. Single-cell profiling of inflammatory cells in blood could clarify these PAH mechanisms.

METHODS: We evaluated a University of Pittsburgh Medical Center cohort consisting of idiopathic PAH (iPAH) and systemic sclerosis-associated PAH (sscPAH) patients and non-PAH controls. We performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from controls (n=3) and from PAH patients (iPAH and sscPAH) naïve to treatment (n=4), PAH patients 3 months after phosphodiesterase-5 inhibitor (PDE5i) treatment (n=7) and PAH patients 3 months after PDE5i+macitentan treatment (n=6). We compared the transcriptomes of five PBMC subtypes from iPAH and sscPAH to observe their serial responses to treatments. Furthermore, we utilised network analysis to illuminate the altered connectivity of biological networks in this complex disease.

RESULTS: We defined differential gene expression and perturbed network connectivity in PBMCs of PAH patients following treatment with PDE5i or PDE5i+macitentan. Importantly, we identified significant reversal of inflammatory transcripts and pathways in the combined PAH patient cohort after vasodilator therapy in every PBMC type assessed. The "glucagon signalling in metabolic regulation" pathway in monocytes was reversed after vasodilator therapy via two independent analysis modalities.

CONCLUSION: Via a systems-biology approach, we define inflammatory reprogramming in the blood of PAH patients and the anti-inflammatory activity of vasodilators. Such findings establish diagnostic and prognostic blood-based tools for tracking inflammatory progression of PAH and response to therapy.

PMID:39811555 | PMC:PMC11726584 | DOI:10.1183/23120541.00486-2024

Cell Prolif. 2025 Jan 14:e13772. doi: 10.1111/cpr.13772. Online ahead of print.

ABSTRACT

Due to the similarity to human hepatocytes, porcine hepatocytes play an important role in hepatic research and drug evaluation. However, once hepatocytes were cultured in vitro, it was often prone to dedifferentiate, resulting in the loss of their characteristic features and normal functions, which impede their application in liver transplantation and hepatotoxic drugs evaluation. Up to now, this process has yet to be thoroughly investigated from the single-cell resolution and multi-omics perspective. In this study, we utilized 10× multiome technology to dissect the heterogeneity of porcine hepatocytes at different time points (Days 0, 1, 3, 5 and 7) during dedifferentiation. We comprehensively investigated cell heterogeneity, cellular dynamics, signalling pathways, potential gene targets, enhancer-driven gene regulatory networks, cell-cell communications of these cells and the conservation of mechanisms across species. We found that a series of critical signalling pathways driven by ERK, PI3K, Src and TGF-β were activated during this process, especially in the early stage of dedifferentiation. Based on these discoveries, we constructed a chemical combination targeting these pathways, which effectively inhibited the dedifferentiation of porcine hepatocytes in vitro. To validate the effectiveness of this combination, we transplanted such treated hepatocytes into FRGN mice, and the results demonstrated that these cells could effectively repopulate the liver and improve the survival of mice.

PMID:39810466 | DOI:10.1111/cpr.13772