Bioresour Technol. 2025 Jan 12:132064. doi: 10.1016/j.biortech.2025.132064. Online ahead of print.

ABSTRACT

Pinene is a plant volatile monoterpenoid which is used in the fragrance, pesticide, and biofuel industries. Although α-pinene has been synthesized in microbial cell factories, the low synthesis efficiency has thus far limited its production. In this study, the cell growth and α-pinene production of the engineered yeast were decoupled by a dynamic regulation strategy, resulting in a 101.1-fold increase in α-pinene production compared to the control. By enhancing the mevalonate pathway and expanding the cytosolic acetyl-CoA pool, α-pinene production was further increased. Overexpression of the transporter Sge1 resulted in a redistribution of global gene transcription, leading to an increased flux of α-pinene synthesis. By optimizing the aeration flow rate in 3-L bioreactors, the α-pinene production reached 1.8 g/L, which is the highest reported α-pinene production in cell factories. Our research provides insights and fundamentals for the efficient synthesis of monoterpenoids in microbial cell factories.

PMID:39809385 | DOI:10.1016/j.biortech.2025.132064

Dev Cell. 2025 Jan 9:S1534-5807(24)00771-8. doi: 10.1016/j.devcel.2024.12.032. Online ahead of print.

ABSTRACT

Reactivation of cell division is crucial for the regeneration of damaged tissues, which is a fundamental process across all multicellular organisms. However, the mechanisms underlying the activation of cell division in plants during regeneration remain poorly understood. Here, we show that single-cell endodermal ablation generates a transient change in the local mechanical pressure on neighboring pericycle cells to activate patterned cell division that is crucial for tissue regeneration in Arabidopsis roots. Moreover, we provide strong evidence that this process relies on the phytohormone ethylene. Thus, our results highlight a previously unrecognized role of mechano-chemical control in patterned cell division during regeneration in plants.

PMID:39809282 | DOI:10.1016/j.devcel.2024.12.032

Biomed Pharmacother. 2025 Jan 13;183:117829. doi: 10.1016/j.biopha.2025.117829. Online ahead of print.

ABSTRACT

Soluble guanylate cyclase (sGC) stands as a pivotal regulatory element in intracellular signalling pathways, mediating the formation of cyclic guanosine monophosphate (cGMP) and impacting diverse physiological processes across tissues. Increased formation of reactive oxygen species (ROS) is widely recognized to modulate cGMP signalling. Indeed, oxidatively damaged, and therefore inactive sGC, contributes to poor vascular reactivity and more severe neurological damage upon stroke. However, the specific involvement of cGMP in redox signalling remains elusive. Here, we demonstrate a significant cGMP-dependent reduction of reactive oxygen and nitrogen species upon sGC activation under hypoxic conditions, independent of any potential scavenger effects. Importantly, this reduction is directly mediated by downregulating NADPH oxidase (NOX) 4 and 5 during reperfusion. Using an in silico simulation approach, we propose a mechanistic link between increased cGMP signalling and reduced ROS formation, pinpointing NF-κB1 and RELA/p65 as key transcription factors regulating NOX4/5 expression. In vitro studies revealed that p65 translocation to the nucleus was reduced in hypoxic human microvascular endothelial cells following sGC activation. Altogether, these findings unveil the intricate regulation and functional implications of sGC, providing valuable insights into its biological significance and ultimately therapeutic potential.

PMID:39809128 | DOI:10.1016/j.biopha.2025.117829

Comput Biol Med. 2025 Jan 13;186:109665. doi: 10.1016/j.compbiomed.2025.109665. Online ahead of print.

ABSTRACT

BACKGROUND: Effective drugs for tendinopathy are lacking, resulting in significant morbidity and re-tearing rate after operation. Applying systems biology to identify new applications for current pharmaceuticals can decrease the duration, expenses, and likelihood of failure associated with the development of new drugs.

METHODS: We identify tendinopathy signature genes employing a transcriptomics database encompassing 154 clinical tendon samples. We then proposed a systems biology based drug prediction strategy that encompassed multiplex transcriptional drug prediction, systematic review assessment, deep learning based efficacy prediction and Mendelian randomization (MR). Finally, we evaluated the effects of drug target using gene knockout mice.

RESULTS: We demonstrate that sirolimus is a repurposable drug for tendinopathy, supported by: 1) Sirolimus achieves top ranking in drug-gene signature-based multiplex transcriptional drug efficacy prediction, 2) Consistent evidence from systematic review substantiates the efficacy of sirolimus in the management of tendinopathy, 3) Genetic prediction indicates that plasma proteins inhibited by mTOR (the target of sirolimus) are associated with increased tendinopathy risk. The effectiveness of sirolimus is further corroborated through in vivo testing utilizing tendon tissue-specific mTOR gene knockout mice. Integrative pathway enrichment analysis suggests that mTOR inhibition can regulate heterotopic ossification-related pathways to ameliorate clinical tendinopathy.

CONCLUSIONS: Our study assimilates knowledge of system-level responses to identify potential drugs for tendinopathy, and suggests sirolimus as a viable candidate. A systems biology approach could expedite the repurposing of drugs for human diseases that do not have well-defined targets.

PMID:39809087 | DOI:10.1016/j.compbiomed.2025.109665

PLoS One. 2025 Jan 14;20(1):e0316548. doi: 10.1371/journal.pone.0316548. eCollection 2025.

ABSTRACT

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model's efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety.

PMID:39808682 | DOI:10.1371/journal.pone.0316548

PLoS One. 2025 Jan 14;20(1):e0317585. doi: 10.1371/journal.pone.0317585. eCollection 2025.

ABSTRACT

Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view images to study the relationship between visual landscape features and human perception of urban waterfront areas, employing linear regression and random forest models to predict human perception along urban coastal roads. Based on aesthetic and distinctiveness perception, urban coastal roads in Xiamen were classified into four types with different emphasis and priorities for improvement. The results showed that: 1) the degree of coastal openness had the greatest influence on human perception while the coastal landscape with a high green visual index decreases the distinctiveness perception; 2) the random forest model can effectively predict human perception on urban coastal roads with an accuracy rate of 87% and 77%; 3) The proportion of low perception road sections with potential for improvement is 60.6%, among which the proportion of low aesthetic perception and low distinctiveness perception road sections is 10.5%. These findings offer crucial evidence regarding human perception of urban coastal roads, and can provide targeted recommendations for enhancing the visual environment of urban coastal road landscapes.

PMID:39808675 | DOI:10.1371/journal.pone.0317585

J Thorac Imaging. 2024 Sep 20. doi: 10.1097/RTI.0000000000000808. Online ahead of print.

ABSTRACT

PURPOSE: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

MATERIALS AND METHODS: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient. It is part of a fully automatic pipeline for the detection, segmentation, matching, and classification of metastatic lung lesions in longitudinal chest CT scans. A data set of 5040 metastatic lung lesions in 344 pairs of 208 prior and current chest CT scans from 79 patients was used for training/validation (173 scans, 65 patients) and testing (35 scans, 14 patients) of a standalone 3D U-Net models and 3 simultaneous SimU-Net models. Outcome measures were the lesion detection and segmentation precision, recall, Dice score, average symmetric surface distance (ASSD), lesion matching, and classification of lesion changes from computed versus manual ground-truth annotations by an expert radiologist.

RESULTS: SimU-Net achieved a mean lesion detection recall and precision of 0.93±0.13 and 0.79±0.24 and a mean lesion segmentation Dice and ASSD of 0.84±0.09 and 0.33±0.22 mm. These results outperformed the standalone 3D U-Net model by 9.4% in the recall, 2.4% in Dice, and 15.4% in ASSD, with a minor 3.6% decrease in precision. The SimU-Net pipeline achieved perfect precision and recall (1.0±0.0) for lesion matching and classification of lesion changes.

CONCLUSIONS: Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.

PMID:39808543 | DOI:10.1097/RTI.0000000000000808

J Neuroophthalmol. 2024 Dec 1;44(4):462-468. doi: 10.1097/WNO.0000000000002229. Epub 2024 Aug 1.

ABSTRACT

BACKGROUND: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON.

METHODS: This was a retrospective study of patients with ON seen at our institution between 2007 and 2022. Fundus photographs (1,599) were retrospectively collected from a total of 321 patients classified into 2 groups: MS ON (262 patients; 1,114 photographs) and non-MS ON (59 patients; 485 photographs). The dataset was divided into training and holdout test sets with an 80%/20% ratio, using stratified sampling to ensure equal representation of MS ON and non-MS ON patients in both sets. Model hyperparameters were tuned using 5-fold cross-validation on the training dataset. The overall performance and generalizability of the model was subsequently evaluated on the holdout test set.

RESULTS: The receiver operating characteristic (ROC) curve for the developed model, evaluated on the holdout test dataset, yielded an area under the ROC curve of 0.83 (95% confidence interval [CI], 0.72-0.92). The model attained an accuracy of 76.2% (95% CI, 68.4-83.1), a sensitivity of 74.2% (95% CI, 55.9-87.4) and a specificity of 76.9% (95% CI, 67.6-85.0) in classifying images as non-MS-related ON.

CONCLUSIONS: This study provides preliminary evidence supporting a role for AI in differentiating non-MS ON subtypes from MS ON. Future work will aim to increase the size of the dataset and explore the role of combining clinical and paraclinical measures to refine deep learning models over time.

PMID:39808513 | DOI:10.1097/WNO.0000000000002229

JCO Clin Cancer Inform. 2025 Jan;9:e2300248. doi: 10.1200/CCI.23.00248. Epub 2025 Jan 14.

ABSTRACT

PURPOSE: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.

METHODS: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency. The fits were also compared against previously published results from institutions using the full resolution series. The model was then evaluated on a separate cohort for validity of biomarker indications. Finally, the model was used as a fundamental part of predicting response to neoadjuvant chemotherapy (NACT).

RESULTS: Temporally subsampled DCE-MRI series yield perfusion fit variations on the scale of 1% of the tumor median value when input frames are varied. Fits generated from pseudoclinical series are within the variation range seen between imaging sites (ρ = 0.55), voxel-wise. The model also demonstrates significant correlations with orthogonal positron emission tomography imaging, indicating potential for use as a biomarker proxy. Specifically, using the perfusion fits as the grounding for a biophysical simulation of response, we correctly predict the pathologic complete response status after NACT in 15 of 18 patients, for an accuracy of 0.83, with a specificity and sensitivity of 0.83 as well.

CONCLUSION: Clinical DCE-MRI data may be leveraged to provide stable perfusion fit results and indirectly interrogate the tumor microenvironment. These fits can then be used downstream for prediction of response to NACT with high accuracy.

PMID:39808751 | DOI:10.1200/CCI.23.00248

ACS Synth Biol. 2025 Jan 14. doi: 10.1021/acssynbio.4c00290. Online ahead of print.

ABSTRACT

Naturally evolved and synthetically designed forms of compartmentalization benefit encapsulated function by increasing local concentrations of substrates and protecting cargo from destabilizing environments and inhibitors. Crucial to understanding the fundamental principles of compartmentalization are experimental systems enabling the measurement of the permeability rates of small molecules. Here, we report the experimental measurement of the small-molecule permeability of a 40 nm icosahedral bacterial microcompartment shell. This was accomplished by heterologous loading of light-producing luciferase enzymes and kinetic measurement of luminescence using stopped-flow spectrophotometry. Compared to free enzyme, the luminescence signal kinetics was slower when the luciferase was encapsulated in bacterial microcompartment shells. The results indicate that substrates and products can still exchange across the shell, and modeling of the experimental data suggest that a 50× permeability rate increase occurs when shell vertices were vacant. Overall, our results suggest design considerations for the construction of heterologous bacterial microcompartment shell systems and compartmentalized function at the nanoscale.

PMID:39808735 | DOI:10.1021/acssynbio.4c00290

Proc Natl Acad Sci U S A. 2025 Jan 21;122(3):e2409724122. doi: 10.1073/pnas.2409724122. Epub 2025 Jan 14.

ABSTRACT

ADAR is highly expressed and correlated with poor prognosis in hepatocellular carcinoma (HCC), yet the role of its constitutive isoform ADARp110 in tumorigenesis remains elusive. We investigated the role of ADARp110 in HCC and underlying mechanisms using clinical samples, a hepatocyte-specific Adarp110 knock-in mouse model, and engineered cell lines. ADARp110 is overexpressed and associated with poor survival in both human and mouse HCC. It creates an immunosuppressive microenvironment by inhibiting total immune cells, particularly cytotoxic GZMB+CD8+ T cells infiltration, while augmenting Treg cells, MDSCs, and exhausted CD8+ T cells ratios. Mechanistically, ADARp110 interacts with SNRPD3 and RNPS1 to stabilize CD24 mRNA by inhibiting STAU1-mediated mRNA decay. CD24 protects HCC cells from two indispensable mechanisms: macrophage phagocytosis and oxidative stress. Genetic knockdown or monoclonal antibody treatment of CD24 inhibits ADARp110-overexpressing tumor growth. Our findings unveil different mechanisms for ADARp110 modulation of tumor immune microenvironment and identify CD24 as a promising therapeutic target for HCCs.

PMID:39808660 | DOI:10.1073/pnas.2409724122

Discov Oncol. 2025 Jan 14;16(1):44. doi: 10.1007/s12672-025-01779-x.

ABSTRACT

BACKGROUND: Thyroid carcinoma (THCA) is the most common cancer of the endocrine system. Natural killer (NK) cell play an important role in tumor immune surveillance. The aim of this study was to explore the possible molecular mechanisms involved in NK cell in THCA to help the management and treatment of the disease.

METHODS: All data were downloaded from public databases. Candidate hub genes associated with NK cell in THCA were identified by limma, WGCNA and singleR packages. Functional enrichment analysis was performed on the candidate hub genes. Hub genes associated with NK cell were identified by Pearson correlation analysis. The mRNA-miRNA-lncRNA and transcription factors (TF) networks were constructed and the drug was predicted.

RESULTS: The infiltration level of NK cell in THCA tissues was higher than that in paracancerous tissues. KEGG functional enrichment analysis only obtained two signaling pathways, thyroid hormone synthesis and mineral absorption. CTSC, FN1, SLC34A2 and TMSB4X identified by Pearson correlation analysis were considered as the hub genes. Receiver operating characteristic analysis suggested that hub genes may be potential diagnostic biomarkers. In mRNA-miRNA-lncRNA network, FN1 had the highest correlation with IQCH-AS1, and IQCH-AS1 was also correlated with hsa-miR-543. In addition, FN1 and RUNX1 were also found to have the highest correlation in TF network. Finally, NK cell-related drugs belinostat and vorinostat were identified based on ASGARD.

CONCLUSION: The identification of important signaling pathways, molecules and drugs provides potential research directions for further research in THCA and contributes to the development of diagnostic and therapeutic approaches for this disease.

PMID:39808350 | DOI:10.1007/s12672-025-01779-x

Clin Transl Sci. 2025 Jan;18(1):e70125. doi: 10.1111/cts.70125.

ABSTRACT

Monoclonal antibodies (mAbs) are critical components in the therapeutic landscape, but their dosing strategies often evolve post-approval as new data emerge. This review evaluates post-marketing label changes in dosing information for FDA-approved mAbs from January 2015 to September 2024, with a focus on both initial and extended indications. We systematically analyzed dosing modifications, categorizing them into six predefined groups: Dose increases or decreases, inclusion of new patient populations by body weight or age, shifts from body weight-based dosing to fixed regimens, and adjustments in infusion rates. Among the 86 mAbs evaluated, 21% (n = 18) exhibited changes in dosing information for the initial indication, with a median time to modification of 37.5 months (range: 5-76 months). Furthermore, for mAbs with extended indications (n = 26), 19.2% (n = 5) underwent dosing changes in their first extensions, with a median time to adjustment of 31 months (range: 8-71 months). Key drivers for these adjustments included optimizing therapeutic efficacy, addressing safety concerns, accommodating special populations, and enhancing patient convenience. We also discuss the role of model-informed drug development, real-world evidence, and pharmacogenomics in refining mAb dosing strategies. These insights underscore the importance of ongoing monitoring and data integration in the post-marketing phase, providing a foundation for future precision medicine approaches in mAb therapy.

PMID:39807701 | DOI:10.1111/cts.70125

Insights Imaging. 2025 Jan 14;16(1):17. doi: 10.1186/s13244-025-01898-7.

ABSTRACT

OBJECTIVES: To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.

METHODS: A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.

RESULTS: The DSC of the test set of the segmentation model was 0.900 (0.810-0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).

CONCLUSION: The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.

CRITICAL RELEVANCE STATEMENT: A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.

KEY POINTS: Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes.

PMID:39808346 | DOI:10.1186/s13244-025-01898-7

Brief Bioinform. 2024 Nov 22;26(1):bbaf001. doi: 10.1093/bib/bbaf001.

ABSTRACT

This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection. However, existing AI-based approaches are primarily binary classifiers, lacking specificity in identifying viral types and reliant on nucleotide sequences. To address these limitations, VirDetect-AI, a novel tool specifically designed for the identification of eukaryotic viruses within metagenomic datasets, is introduced. The VirDetect-AI model employs a combination of convolutional neural networks and residual neural networks to effectively extract hierarchical features and detailed patterns from complex amino acid genomic data. The results demonstrated that the model has outstanding results in all metrics, with a sensitivity of 0.97, a precision of 0.98, and an F1-score of 0.98. VirDetect-AI improves our comprehension of viral ecology and can accurately classify metagenomic sequences into 980 viral protein classes, hence enabling the identification of new viruses. These classes encompass an extensive array of viral genera and families, as well as protein functions and hosts.

PMID:39808116 | DOI:10.1093/bib/bbaf001

Radiology. 2025 Jan;314(1):e240238. doi: 10.1148/radiol.240238.

ABSTRACT

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Materials and Methods Male patients with suspected prostate cancer who underwent multiparametric MRI were retrospectively included from three centers from April 2020 to April 2023. A deep learning model (pix2pix algorithm) was trained to synthesize contrast-enhanced MRI scans from four noncontrast MRI sequences (T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps) and then tested on an internal and two external datasets. The reference standard for model training was the second postcontrast phase of the dynamic contrast-enhanced sequence. Similarity between simulated and acquired contrast-enhanced images was evaluated using the multiscale structural similarity index. Three radiologists independently scored T2-weighted and diffusion-weighted MRI with either simulated or acquired contrast-enhanced images using PI-RADS, version 2.1; agreement was assessed with Cohen κ. Results A total of 567 male patients (mean age, 66 years ± 11 [SD]) were divided into a training test set (n = 244), internal test set (n = 104), external test set 1 (n = 143), and external test set 2 (n = 76). Simulated and acquired contrast-enhanced images demonstrated high similarity (multiscale structural similarity index: 0.82, 0.71, and 0.69 for internal test set, external test set 1, and external test set 2, respectively) with excellent reader agreement of PI-RADS scores (Cohen κ, 0.96; 95% CI: 0.94, 0.98). When simulated contrast-enhanced imaging was added to biparametric MRI, 34 of 323 (10.5%) patients were upgraded to PI-RADS 4 from PI-RADS 3. Conclusion It was feasible to generate simulated contrast-enhanced prostate MRI using deep learning. The simulated and acquired contrast-enhanced MRI scans exhibited high similarity and demonstrated excellent agreement in assessing clinically significant prostate cancer based on PI-RADS, version 2.1. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Neji and Goh in this issue.

PMID:39807983 | DOI:10.1148/radiol.240238

JCO Clin Cancer Inform. 2025 Jan;9:e2400325. doi: 10.1200/CCI-24-00325. Epub 2025 Jan 14.

NO ABSTRACT

PMID:39807853 | DOI:10.1200/CCI-24-00325

Appl Biochem Biotechnol. 2025 Jan 14. doi: 10.1007/s12010-024-05168-y. Online ahead of print.

ABSTRACT

Dysregulated circular RNAs (circRNAs) has been revealed to be involved in pulmonary fibrosis progression. Herein, this study focused on exploring the function and mechanism of circRNA Zinc Finger MYM-Type Containing 2 (circZMYM2) on idiopathic pulmonary fibrosis (IPF) using transforming growth factor (TGF)-β1-stimulated fibroblasts. Human fibroblast cell lines IMR-90 and HFL1 were stimulated with TGF-β1 to mimic fibrosis condition in vitro. Levels of genes and proteins were detected by qRT-PCR and western blotting. Cell proliferation and migration were analyzed using cell counting kit-8 assay, 5-Ethynyl-2'-deoxyuridine (EdU) and wound healing assays. The fibrosis progression was determined by the change of E-cadherin, α-smooth muscle actin (α-SMA), collagen type I α 1 (COL1A1) and collagen type III α 1 (COL3A1). The interaction between miR-199b-5p and circZMYM2 or KLF13 (Kruppel Like Factor 13) was analyzed using dual-luciferase reporter, RIP and RNA-pull-down assays. CircZMYM2 was decreased in TGF-β1-induced IMR-90 and HFL1 fibroblasts. Functionally, re-expression of circZMYM2 in IMR-90 and HFL1 cells could attenuate TGF-β1-evoked proliferation, migration and fibrosis in cells. Mechanistically, the circZMYM2/miR-199b-5p/KLF13 constituted a competing endogenous RNA (ceRNA). TGF-β1 reduced KLF13 expression and increased miR-199b-5p expression in IMR-90 and HFL1 cells. Further rescue experiments suggested that miR-199b-5p up-regulation or KLF13 knockdown reversed the anti-fibrotic effects of circZMYM2; moreover, silencing of miR-199b-5p exhibited anti-fibrotic effects, which was counteracted by KLF13 knockdown. CircZMYM2 had an anti-fibrotic effect that could suppress fibroblast activation via miR-199b-5p/KLF13 axis, pointing a novel perspective into the potential action pattern of circ_0022383 in IPF.

PMID:39808406 | DOI:10.1007/s12010-024-05168-y

Clin Exp Rheumatol. 2025 Jan 14. doi: 10.55563/clinexprheumatol/tjnyz5. Online ahead of print.

ABSTRACT

OBJECTIVES: The progressive decline in interstitial lung disease associated with non-scleroderma connective tissue disease (ILD-NSCTD) is linked to poor prognosis and frequently results in respiratory failure. Lung transplantation (LTx) offers a viable treatment option, yet its outcomes in ILD-NSCTD remain contentious, particularly across different subtypes.

METHODS: This retrospective cohort study included patients with idiopathic pulmonary fibrosis (IPF) (n=11,610) and ILD-NSCTD (n=610) listed in the United Network for Organ Sharing (UNOS) database who underwent lung transplantation between May 5, 2005, and December 31, 2022. We used the Kaplan-Meier method to evaluate cumulative survival rates and logistic regression to assess the risk of post-operative complications.

RESULTS: Compared to IPF patients, those with ILD-NSCTD are generally younger, with a lower proportion of male and white patients. After propensity matching, overall survival rates remained similar between the groups (log-rank, p=0.953). However, ILD-NSCTD was associated with a significantly higher risk of post-operative stroke (adjusted OR 1.75, 95% CI 1.12-2.74, p=0.015) and longer post-operative hospital stays (p<0.001). Subgroup analyses yielded consistent results. Finally, infection was identified as the leading cause of death.

CONCLUSIONS: Compared to IPF, patients with ILD-NSCTD have a significantly higher risk of post-operative stroke and extended hospital stays, potentially due to complications inherent to ILD-NSCTD. However, the underlying causes of these outcomes remain unclear. Despite these differences, short-term and long-term survival rates are comparable between the two groups, with consistent findings across various ILD-NSCTD subgroups. Therefore, ILD-NSCTD should not be regarded as a relative contraindication for lung transplantation. Nonetheless, the influence of extra-pulmonary complications in ILD-NSCTD patients requires further investigation.

PMID:39808303 | DOI:10.55563/clinexprheumatol/tjnyz5

Immun Inflamm Dis. 2025 Jan;13(1):e70123. doi: 10.1002/iid3.70123.

ABSTRACT

BACKGROUND: Post-coronavirus disease 19 lung fibrosis (PCLF) shares common immunological abnormalities with idiopathic pulmonary fibrosis (IPF), characterized by an unbalanced cytokine profile being associated with the development of lung fibrosis. The aim of the present study was to analyze and compare the different subsets of CD4- and CD8-T cells, along with specific cytokine expression patterns, in peripheral blood (PB) from patients affected by PCLF and IPF and healthy controls (HCs).

METHODS: One-hundred patients followed at the Rare Lung Disease Center of Siena University Hospital were enrolled. Eight HCs were recruited. PB samples were collected, and CD4- and CD8-T subsets were analyzed through flow cytometry. Multiplex bead-based LEGENDplex™ were used for cytokine quantification.

RESULTS: Higher CD8 percentages were observed in IPF than in HCs and PCLF (p = 0.020 and p = 0.007, respectively). PCLF subgroup showed higher Th-naïve, Th-effector, Tc-naïve, and Tc-reg percentages than IPF (p < 0.001; p = 0.018; p = 0.005; p = 0.017, respectively). Th-naïve and Tc-naïve inversely correlated with Tc-reg (p < 0.0001, r = -0.61 and p = 0.005, r = -0.39, respectively). Tc-naïve-PD1 and Tc-effector-PD1 percentages were higher in PCLF than IPF (p < 0.001), while Tfh-reg and Tfc-reg were significantly higher in IPF than PCLF (p < 0.001). IL-4, IL-2, TNF-α, and IL-17A were more expressed in PCLF than IPF (p < 0.001). IL-8 directly correlated with Tc-naïve percentages in PCLF (p = 0.018, r = 0.35).

CONCLUSION: A variety of immune cells is involved in the development and progression of pulmonary fibrosis confirming an immunological similarity between IPF and PCLF. T-reg cells play a key role in the worsening of the disease. High cytokine values showed a pro-fibrotic environment in PCLF patients, suggesting dysregulation of the immune system of these patients. Moreover, the immunological similarity between IPF and PCLF patients suggests that SARS-CoV2 infection may trigger the activation of biological pathways common with IPF.

PMID:39807767 | DOI:10.1002/iid3.70123