J Cyst Fibros. 2025 Jan 11:S1569-1993(25)00005-0. doi: 10.1016/j.jcf.2025.01.005. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic Fibrosis-related Bone Disease is an emerging challenge faced by 50 % of adult people with cystic fibrosis (CF). The multifactorial causes of this comorbidity remain elusive. However, congenital bone defects have been observed in animal models with CFTR mutations, suggesting its importance. The role of CFTR in bone cells development is unknown. Studies from human cells remain somewhat controversial depending on the cells used and the disease state of the patients from which the cells derived.

METHODS: Therefore, we investigated the role of CFTR in osteoblast development using induced pluripotent stem cells generated from homozygous CF donors for F508del and non-CF controls. This approach allows for a clear understanding towards how the CFTR mutation may influence osteoblast differentiation independently from other confounding factors.

RESULTS: We observed a lower capacity of differentiation in CF cells as compared to control, already from mesenchymal stem cells (MSC) stage, whereby they retained expression of the pluripotency marker OCT4. Furthermore, our results demonstrated a delayed osteoblast commitment and altered expression of specific markers, such as an increased RANKL/OPG ratio and decreased BMP2, suggesting a potentially perturbed bone homeostasis associated with CFTR mutation.

CONCLUSIONS: This is the first study of its kind, clearly demonstrating a role for CFTR mutation in delaying osteoblast differentiation and/or regeneration.

PMID:39800643 | DOI:10.1016/j.jcf.2025.01.005

J Cyst Fibros. 2025 Jan 11:S1569-1993(24)01852-6. doi: 10.1016/j.jcf.2024.12.001. Online ahead of print.

ABSTRACT

INTRODUCTION: The Lung Clearance Index (LCI) is an established research test, but its role in clinical decision-making is not well defined. This study estimated the proportion of treatment decisions that are changed or supported by the added information provided by LCI.

METHODS: A mixed methods prospective observational study was conducted in North America. Providers were invited to participate in a clinical vignette survey consisting of 10 hypothetical scenarios involving pediatric cystic fibrosis (CF) management. First, they made a clinical decision based on information captured in routine clinical visits. Then, the LCI value was made available, and providers were asked whether the LCI changed or supported their decision. A prospective study was also conducted at three CF centres to determine how often physicians make pulmonary treatment decisions at CF clinic visits and how often they perceive additional lung function data would be helpful for these decisions.

RESULTS: We received 522 vignette responses from 62 participants. LCI changed the decision in 18.4 % of cases, supported the decision in 57.1 % and did not impact the decision in 24.5 %. Data from patient encounters in the prospective study demonstrated that changes to pulmonary treatments were considered in 98/322 (30.4 %) visits; additional lung function information could potentially have helped in 64.3 % of the treatment decisions.

CONCLUSION: LCI changes or supports a significant proportion of treatment decisions. Providers perceive that additional information about lung function could be helpful at the majority of encounters where changes in treatment are considered.

PMID:39800642 | DOI:10.1016/j.jcf.2024.12.001

Dig Liver Dis. 2025 Jan 11:S1590-8658(24)01145-9. doi: 10.1016/j.dld.2024.12.020. Online ahead of print.

ABSTRACT

BACKGROUND: In pediatric patients, celiac disease (CD) may influence the health-related quality of life (HRQoL).

AIMS: The study aimed to assess HRQoL and further characterise the clinical factors associated with reduced HRQoL, in a large multicenter pediatric cohort with CD.

METHODS: The disease-specific questionnaire CD Dutch Questionnaire (CDDUX) and the generic questionnaire Paediatric Quality of Life Inventory (PedsQL) were used to assess the HRQoL. Clinical and sociodemographic characteristics were analyzed, univariate and multivariate analysis were conducted.

RESULTS: Eleven different Italian pediatric centers and 871 families were involved. Mean age at interview was 12.9 ± 2.9 years. The mean total CDDUX score of CD patients was 47.1 ± 18.8, revealing a neutral HRQoL (47.1 ± 18.8), and a good to very good HRQoL according to the PedsQL (81.4 ± 12.6), parents indicated lower scores (p = 0.03) with both questionnaires (CDDUX 45.1 ± 18.6 and PedsQL 79.9 ± 14.5). Patients with lower HRQoL were mainly female, living in Northern Italy, with lower parent's education level and non-biopsy diagnosis of CD. In multivariate analysis, the main predictor of lower CDDUX score was non-biopsy diagnosis.

CONCLUSIONS: The HRQoL in a large cohort of Italian children is reported as neutral-good. This indicates a high level of adaptive behaviors in response to the daily challenges of CD. Parents tend to underestimate their children's HRQoL. Specific clinical factors, including non-biopsy diagnosis, may be associated to lower HRQoL.

PMID:39800588 | DOI:10.1016/j.dld.2024.12.020

J Control Release. 2025 Jan 10:S0168-3659(25)00019-7. doi: 10.1016/j.jconrel.2025.01.017. Online ahead of print.

ABSTRACT

Aerosol delivery represents a rapid and non-invasive way to directly reach the lungs while escaping the hepatic first-pass effect. The development of pulmonary drugs for respiratory diseases such as cystic fibrosis, lung infections, pulmonary fibrosis or lung cancer requires an enhanced understanding of the relationships between the natural physiology of the respiratory system and the pathophysiology of these conditions. This knowledge is crucial to better predict and thereby control drug deposition. Moreover, aerosol administration faces several challenges, including the pulmonary tract, immune system, mucociliary clearance, the presence of fluid on the airway surfaces, and, in some cases, bacterial colonisation. Each of them directly influences on the bioavailability of the active molecule. In addition to these challenges, particle size and the device used to administer the treatment are critical factors that can significantly impact the biodistribution of the drugs. Nanoparticles are very promising in the development of new formulations for aerosol drug delivery, as they can be fine-tuned to reach the entire pulmonary tract and overcome the difficulties encountered along the way. However, to properly assess drug delivery, preclinical studies need to be more thorough to efficiently enhance drug delivery.

PMID:39800241 | DOI:10.1016/j.jconrel.2025.01.017

Brief Bioinform. 2024 Nov 22;26(1):bbae719. doi: 10.1093/bib/bbae719.

ABSTRACT

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells. Integrating such additional data from other modalities can effectively enhance spatial transcriptomics data, and, conversely, spatial transcriptomics data can supplement scRNA-seq with spatial information. Moreover, the rapid development of spatial multi-omics technology has spurred the demand for the integration of spatial multi-omics data to present a more detailed molecular landscape within tissues. Numerous deep learning (DL) methods have been developed for integrating spatial transcriptomics with other modalities. However, a comprehensive review of DL approaches for integrating spatial transcriptomics data with other modalities remains absent. In this study, we systematically review the applications of DL in integrating spatial transcriptomics data with other modalities. We first delineate the DL techniques applied in this integration and the key tasks involved. Next, we detail these methods and categorize them based on integrated modality and key task. Furthermore, we summarize the integration strategies of these integration methods. Finally, we discuss the challenges and future directions in integrating spatial transcriptomics with other modalities, aiming to facilitate the development of robust computational methods that more comprehensively exploit multimodal information.

PMID:39800876 | DOI:10.1093/bib/bbae719

Brief Bioinform. 2024 Nov 22;26(1):bbae717. doi: 10.1093/bib/bbae717.

ABSTRACT

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy. DD-PRiSM consists of two predictive models. The first is the Monotherapy model, which predicts parameters of the drug response curve based on drug structure and cell line gene expression. This reconstructed curve is then used to predict cell viability at the given drug dosage. The second is the Combination therapy model, which predicts the efficacy of drug combinations by analyzing individual drug effects and their synergistic interactions with a specific dosage level of individual drugs. The efficacy of DD-PRiSM is demonstrated through its performance metrics, achieving a root mean square error of 0.0854, a Pearson correlation coefficient of 0.9063, and an R2 of 0.8209 for unseen pairs. Furthermore, DD-PRiSM distinguishes itself by its capability to decompose combination therapy efficacy, successfully identifying synergistic drug pairs. We demonstrated synergistic responses vary across cancer types and identified hub drugs that trigger synergistic effects. Finally, we suggested a promising drug pair through our case study.

PMID:39800875 | DOI:10.1093/bib/bbae717

Brief Bioinform. 2024 Nov 22;26(1):bbae711. doi: 10.1093/bib/bbae711.

ABSTRACT

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.

PMID:39800872 | DOI:10.1093/bib/bbae711

Environ Sci Pollut Res Int. 2025 Jan 13. doi: 10.1007/s11356-024-35764-8. Online ahead of print.

ABSTRACT

Human-induced global warming, primarily attributed to the rise in atmospheric CO2, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support vector machine (SVM), random forest (RF), and gradient boosting (GB)), and seven deep learning models (artificial neural network (ANN), recurrent neural network variations such as gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional-LSTM (BILSTM), and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R2, MAE, RMSE, and MAPE). The results show that the machine learning (ML) and deep learning (DL) models, with higher R2 (0.714-0.932) and lower RMSE (0.480-0.247) values, respectively, outperformed the statistical model, which had R2 (- 0.060-0.719) and RMSE (1.695-0.537) values, in predicting daily CO2 emissions across all four regions. The performance of the ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns from which the model can learn. Additionally, applying ensemble techniques such as bagging and voting improved the performance of the ML models by approximately 9.6%, whereas hybrid combinations of CNN-RNN enhanced the performance of the RNN models. In summary, the performance of both the ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO2 emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO2 emission reduction.

PMID:39800837 | DOI:10.1007/s11356-024-35764-8

Breast Cancer Res. 2025 Jan 12;27(1):6. doi: 10.1186/s13058-025-01959-1.

ABSTRACT

Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective. To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. Our classifier model accurately identified the precise phenotypic status, achieving an area under the curve (AUC) of 0.886 at the tile-level and an AUC of 0.910 at the slide-level. Furthermore, we evaluated the efficacy of the classifier in predicting endocrine response using data from an independent ER+ breast cancer patient cohort. Our classifier achieved a predicting accuracy of 81.25%, and 88.7% slides labeled as endocrine resistant were predicted as the mesenchymal-phenotype, while 75.6% slides labeled as sensitive were predicted as the epithelial-phenotype. Our work introduces an H&E-based framework capable of accurately predicting EMT phenotype and endocrine response for ER+ breast cancer, demonstrating its potential for clinical application and benefit.

PMID:39800743 | DOI:10.1186/s13058-025-01959-1

Global Spine J. 2025 Jan 12:21925682251314379. doi: 10.1177/21925682251314379. Online ahead of print.

ABSTRACT

STUDY DESIGN: Systematic review.

OBJECTIVE: Artificial intelligence (AI) and deep learning (DL) models have recently emerged as tools to improve fracture detection, mainly through imaging modalities such as computed tomography (CT) and radiographs. This systematic review evaluates the diagnostic performance of AI and DL models in detecting cervical spine fractures and assesses their potential role in clinical practice.

METHODS: A systematic search of PubMed/Medline, Embase, Scopus, and Web of Science was conducted for studies published between January 2000 and July 2024. Studies that evaluated AI models for cervical spine fracture detection were included. Diagnostic performance metrics were extracted and included sensitivity, specificity, accuracy, and area under the curve. The PROBAST tool assessed bias, and PRISMA criteria were used for study selection and reporting.

RESULTS: Eleven studies published between 2021 and 2024 were included in the review. AI models demonstrated variable performance, with sensitivity ranging from 54.9% to 100% and specificity from 72% to 98.6%. Models applied to CT imaging generally outperformed those applied to radiographs, with convolutional neural networks (CNN) and advanced architectures such as MobileNetV2 and Vision Transformer (ViT) achieving the highest accuracy. However, most studies lacked external validation, raising concerns about the generalizability of their findings.

CONCLUSIONS: AI and DL models show significant potential in improving fracture detection, particularly in CT imaging. While these models offer high diagnostic accuracy, further validation and refinement are necessary before they can be widely integrated into clinical practice. AI should complement, rather than replace, human expertise in diagnostic workflows.

PMID:39800538 | DOI:10.1177/21925682251314379

Open Heart. 2025 Jan 11;12(1):e002998. doi: 10.1136/openhrt-2024-002998.

ABSTRACT

PURPOSE: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.

METHODS: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation. Heatmaps were generated to indicate critical areas identified by the models, aiding clinicians in understanding the model's decision-making process.

RESULTS: Among the 900 CMR cases, 279 involved the LAD artery, 259 the RCA artery and 253 the LCX artery. EfficientNet models outperformed others, with EfficientNetB3 and EfficientNetB0 demonstrating the highest accuracy for LAD, EfficientNetB2 for RCA and EfficientNetB0 for LCX. The area under the curve for receiver operating characteristic (AUROC) reached 0.95 for moderate and 0.94 for severe stenosis in the LAD. For the RCA, the AUROC was 0.92 for both moderate and severe stenosis detection. The LCX achieved an AUROC of 0.88 for the detection of moderate stenoses, though the calibration curve exhibited significant overestimation. Calibration curves matched probabilities for the LAD but showed discrepancies for the RCA. Heatmap visualisations confirmed the models' precision in delineating stenotic lesions. Decision curve analysis and net reclassification index assessments reinforced the efficacy of EfficientNet models, confirming their superior diagnostic capabilities.

CONCLUSION: Our end-to-end deep-learning model demonstrates, for the LAD artery, excellent discriminatory ability and calibration during internal validation, despite a small dataset used to train the network. The model reliably produces precise, highly interpretable images.

PMID:39800435 | DOI:10.1136/openhrt-2024-002998

Int J Radiat Oncol Biol Phys. 2025 Jan 10:S0360-3016(25)00002-1. doi: 10.1016/j.ijrobp.2024.12.026. Online ahead of print.

ABSTRACT

PURPOSE: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning.

MATERIALS AND METHODS: This study included a total of 102 US-planned BT images from a single institution and split into three groups: 50 for model training and validation, 11 to evaluate reconstruction accuracy (test set), and 41 to evaluate the AI tool in a clinical implementation (clinical set). Reconstruction accuracy for the test set was evaluated by comparing the performance of AI-derived and manually reconstructed needles from 5 medical physicists on the 3D-US scans after treatment. The needle total reconstruction time for the clinical set was defined as the timestamp difference from scan acquisition to the start of dose calculations and was compared to values recorded before the clinical implementation of the AI-assisted tool.

RESULTS: A mean error of (0.44±0.32)mm was found between the AI-reconstructed and the human consensus needle positions in the test set, with 95.0% of AI needle points falling below 1mm from their human-made counterparts. Post-hoc analysis showed only one of the human observers' reconstructions were significantly different from the others including the AI's. In the clinical set, the AI algorithm achieved a true positive reconstruction rate of 93.4% with only 4.5% of these needles requiring manual corrections from the planner before dosimetry. Total time required to perform AI-assisted catheter reconstruction on clinical cases was on average 15.2min lower (p < 0.01) compared to procedure without AI assistance.

CONCLUSIONS: This study demonstrates the feasibility of an AI-assisted needle reconstructing tool for 3D-US based HDR prostate BT. This is a step toward treatment planning automation and increased efficiency in HDR prostate BT.

PMID:39800329 | DOI:10.1016/j.ijrobp.2024.12.026

Neuroimage. 2025 Jan 10:121002. doi: 10.1016/j.neuroimage.2025.121002. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting.

MATERIALS (PATIENTS) AND METHODS: A total of 27456 post-Gadolinium-T1 MRI from 132 patients with BM were employed in this study. A deep learning (DL) model was constructed using the PyTorch and PyTorch Lightning frameworks, and the UNETR transfer learning method was employed to segment BM from MRI.

RESULTS: A visual analysis of the AI model results demonstrates confident delineation of the BM lesions. The model shows 100% accuracy in predicting RANO-BM criteria in comparison to that of an expert medical doctor. There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. The user interface developed in this study can readily provide RANO-BM criteria following AI BM segmentation.

CONCLUSION: The in-house deep learning solution is accessible to everyone without expertise in AI and offers effective BM segmentation and substantial time savings.

PMID:39800174 | DOI:10.1016/j.neuroimage.2025.121002

Heart Rhythm. 2025 Jan 10:S1547-5271(25)00019-0. doi: 10.1016/j.hrthm.2024.12.048. Online ahead of print.

ABSTRACT

BACKGROUND: Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value.

OBJECTIVE: This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.

METHODS: We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device. Ground truth for AF presence was established through a benchmark algorithm and expert manual labeling. The Willem Artificial Intelligence (AI) platform, not trained on these ECGs, was used for automatic arrhythmia detection, including AF. A rules-based algorithm was also used for comparison. An expert cardiology committee reviewed false positives and negatives and performance metrics were computed.

RESULTS: The AI platform achieved an accuracy of 96.1% (initial labels) and 96.4% (expert review), with sensitivities of 83.3% and 84.2%, and specificities of 97.3% and 97.6%, respectively. The positive predictive value was 75.2% and 78.0%, and the negative predictive value was 98.4%. Performance of the AI platform largely exceeded the performance of the rules-based algorithm for all metrics. The AI also detected other arrhythmias, such as premature ventricular complexes, premature atrial complexes along with 1-degree atrioventricular blocks.

CONCLUSIONS: The result of this external validation indicates that the AI platform can match cardiologist-level accuracy in AF detection from 1-lead ECGs. Such tools are promising for AF screening and has the potential to improve accuracy in non-cardiology expert healthcare professional interpretation and trigger further tests for effective patient management.

PMID:39800092 | DOI:10.1016/j.hrthm.2024.12.048

Expert Rev Respir Med. 2025 Jan 12. doi: 10.1080/17476348.2025.2453657. Online ahead of print.

ABSTRACT

INTRODUCTION: Interstitial lung disease (ILD) is a broad group of conditions characterized by fibrosis of the lung parenchyma. Idiopathic pulmonary fibrosis (IPF) is the most common subvariant. IPF is marked by considerable symptom burden of dyspnea, cough and fatigue that is often refractory to optimal disease-directed treatment.

AREAS COVERED: In this narrative review, we searched MEDLINE for articles related to the current evidence regarding management of chronic dyspnea, cough, and fatigue as three of the most prevalent and distressing symptoms associated with IPF and other ILDs. Each symptom shares common features of multi-factorial etiology and a lack of safe and effective pharmacological therapies. Both corticosteroids and opioids have been utilized in this context, yet there is insufficient evidence of therapeutic benefit and considerable risk of harms. Whilst some may benefit from symptom-directed pharmacological management, usage must be carefully monitored. Use of non-pharmacological strategies, such as breathing techniques and speech therapy represent low risk and low-cost option, yet broader validation of these therapies' effectiveness is needed.

EXPERT OPINION: Symptom management in IPF and other ILDs requires an iterative and individualized approach. Leveraging the expertise of multidisciplinary teams within an integrated care setting is an important opportunity to maximize health outcomes.

PMID:39800565 | DOI:10.1080/17476348.2025.2453657

Exp Eye Res. 2025 Jan 10:110238. doi: 10.1016/j.exer.2025.110238. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease linked to aging. This study investigates potential connections between IPF and age-related eye problems using a bleomycin-induced IPF mouse model. Intratracheal administration of bleomycin induces rapid lung injury in mice, followed by IPF with characteristics of cellular senescence. IPF-injured mice had reduced amplitudes of scotopic ERG and immunostaining of visual arrestin, suggesting declined rod-related visual function. Interestingly, the mice's eyes also showed increased susceptibility to Staphylococcus aureus infections, reminiscent of the aging eyes. To determine whether an early onset of aging contributes to the eye disorders, we examined complement and senescence markers in the retina. In bleomycin-injury IPF mice, DNA damage-related senescence marker γH2AX was found in the retinal out nuclear layer where photoreceptors are located. Additionally, IPF mice displayed elevated levels of C3b, a complement fragment resulting from C3 activation that occurs frequently in aging eyes. These findings underscore the potential of IPF as a valuable mouse model for investigating early-onset age-related ocular disorders.

PMID:39800285 | DOI:10.1016/j.exer.2025.110238

Nat Commun. 2025 Jan 12;16(1):614. doi: 10.1038/s41467-025-55847-5.

ABSTRACT

Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Testing multiple integration strategies, most of them yield multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrate improved patient risk stratification compared to models built with routine clinical features only. Our study thus provides evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC datasets to develop and validate robust and powerful immunotherapy biomarkers.

PMID:39800784 | DOI:10.1038/s41467-025-55847-5

Signal Transduct Target Ther. 2025 Jan 13;10(1):14. doi: 10.1038/s41392-024-02114-6.

ABSTRACT

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2. (SARS-CoV-2) and its variant strains, including Alpha, Beta, Gamma, Delta, and now Omicron, pose a significant challenge. With the constant evolution of the virus, Omicron and its subtypes BA.1, BA.2, BA.3, BA.4, and BA.5 have developed the capacity to evade neutralization induced by previous vaccination or infection. This evasion highlights the urgency in discovering new monoclonal antibodies (mAbs) with neutralizing activity, especially broadly neutralizing antibodies (bnAbs), to combat the virus.In the current study, we introduced a fully human neutralizing mAb, CR9, that targets Omicron variants. We demonstrated the mAb's effectiveness in inhibiting Omicron replication both in vitro and in vivo. Structural analysis using cryo-electron microscopy (cryo-EM) revealed that CR9 binds to an epitope formed by RBD residues, providing a molecular understanding of its neutralization mechanism. Given its potency and specificity, CR9 holds promise as a potential adjunct therapy for treating Omicron infections. Our findings highlight the importance of continuous mAb discovery and characterization in addressing the evolving threat of COVID-19.

PMID:39800731 | DOI:10.1038/s41392-024-02114-6

Gut Microbes. 2025 Dec;17(1):2446423. doi: 10.1080/19490976.2024.2446423. Epub 2025 Jan 12.

ABSTRACT

The therapeutic benefits of opioids are compromised by the development of analgesic tolerance, which necessitates higher dosing for pain management thereby increasing the liability for drug dependence and addiction. Rodent models indicate opposing roles of the gut microbiota in tolerance: morphine-induced gut dysbiosis exacerbates tolerance, whereas probiotics ameliorate tolerance. Not all individuals develop tolerance, which could be influenced by differences in microbiota, and yet no study design has capitalized upon this natural variation. We leveraged natural behavioral variation in a murine model of voluntary oral morphine self-administration to elucidate the mechanisms by which microbiota influences tolerance. Although all mice shared similar morphine-driven microbiota changes that largely masked informative associations with variability in tolerance, our high-resolution temporal analyses revealed a divergence in the progression of dysbiosis that best explained sustained antinociception. Mice that did not develop tolerance maintained a higher capacity for production of the short-chain fatty acid (SCFA) butyrate known to bolster intestinal barriers and promote neuronal homeostasis. Both fecal microbial transplantation (FMT) from donor mice that did not develop tolerance and dietary butyrate supplementation significantly reduced the development of tolerance independently of suppression of systemic inflammation. These findings could inform immediate therapies to extend the analgesic efficacy of opioids.

PMID:39800714 | DOI:10.1080/19490976.2024.2446423

Chin Med. 2025 Jan 12;20(1):8. doi: 10.1186/s13020-024-01056-z.

ABSTRACT

Network pharmacology plays a pivotal role in systems biology, bridging the gap between traditional Chinese medicine (TCM) theory and contemporary pharmacological research. Network pharmacology enables researchers to construct multilayered networks that systematically elucidate TCM's multi-component, multi-target mechanisms of action. This review summarizes key databases commonly used in network pharmacology, including those focused on herbs, components, diseases, and dedicated platforms for network pharmacology analysis. Additionally, we explore the growing use of network pharmacology in TCM, citing literature from Web of Science, PubMed, and CNKI over the past two decades with keywords like "network pharmacology", "TCM network pharmacology", and "herb network pharmacology". The application of network pharmacology in TCM is widespread, covering areas such as identifying the material basis of TCM efficacy, unraveling mechanisms of action, and evaluating toxicity, safety, and novel drug development. However, challenges remain, such as the lack of standardized data collection across databases and insufficient consideration of processed herbs in research. Questions also persist regarding the reliability of study outcomes. This review aims to offer valuable insights and reference points to guide future research in precision TCM network pharmacology.

PMID:39800680 | DOI:10.1186/s13020-024-01056-z