Clin Transl Sci. 2025 Mar;18(3):e70144. doi: 10.1111/cts.70144.

ABSTRACT

Integration of clinical pharmacogenomics (PGx) within routine cancer care is limited despite frequent use of medicines impacted by PGx, evidence for the benefits of PGx, and the availability of international PGx clinical guidelines. Our study objective was to develop survey tools to assess PGx knowledge, attitudes, practices, perceptions, and education needs among (a) doctors, nurses, and pharmacists involved in cancer care (healthcare professionals, HCPs) and (b) adults who have received cancer treatment or their carers (consumers), with the view to informing implementation strategies for PGx in solid and hematologic cancers. Survey tools were developed in a three-phase (ph) mixed-methods approach. Content was informed by systematic literature review findings and framed by determinants of behavior as informed by the Theoretical Domains Framework (ph-1). Refinement occurred through four separate priority partnership meetings (ph-2). Meetings focused on clinical PGx practices within select cancer streams, and consumers' knowledge, attitudes, and preferences for PGx testing. Content/face validity and health literacy (Flesch Kincaid Grade Level) assessments informed final refinements (ph-3). Separate HCP and consumer survey tools were developed with six common sections: (1) introduction; (2) demographics; (3) experience; (4) knowledge, attitudes, practices and perceptions; (5) education; and (6) vignettes. Content and face validity were rated highly with acceptable health literacy assessments for questions within the consumer survey (median grade level 6; range 1-8). The developed survey tools will be used to generate evidence to inform local implementation strategies for PGx in cancer and promote broader integration of pharmacogenomics in routine clinical care.

PMID:40103279 | DOI:10.1111/cts.70144

J Nanobiotechnology. 2025 Mar 18;23(1):217. doi: 10.1186/s12951-025-03310-4.

ABSTRACT

Non-small cell lung cancer (NSCLC) represents the most prevalent form of lung cancer, exerting a substantial impact on global health. Cisplatin-based chemotherapy is the standard treatment for NSCLC, but resistance and severe side effects present significant clinical challenges. Recently, novel tetravalent platinum compounds have attracted significant interest. While numerous studies concentrate on their functional modifications and targeted delivery, tumor-induced platinum resistance is frequently overlooked. Previous tetravalent platinum compound demonstrated antitumor activity, yet proved ineffective against cells exhibiting resistance to cisplatin. In order to enhance the efficacy and potential applications of tetravalent platinum in NSCLC, a glutathione (GSH)-responsive albumin nanoquadrivalent platinum (HSA@Pt) have been constructed. In light of previous research into drug conjugation, this study was to develop a combined chemo-immunotherapy approach. The HSA@Pt demonstrated high efficacy and low toxicity, with targeted tumor accumulation. Furthermore, Ammonium Tetrathiomolybdate (TM) has been demonstrated to exert a synergistic inhibitory effect on ATPase Copper Transporting Beta (ATP7B) and Programmed Death Ligand 1 (PD-L1), impede platinum efflux, induce cellular stress, and activate antitumor immunity. The findings suggest HSA@Pt's potential for clinical use and a novel chemo-immunotherapy strategy for NSCLC, enhancing the utility of established drugs through synergistic sensitization.

PMID:40102840 | DOI:10.1186/s12951-025-03310-4

Infect Med (Beijing). 2025 Feb 5;4(1):100168. doi: 10.1016/j.imj.2025.100168. eCollection 2025 Mar.

ABSTRACT

Phages, including the viruses that lyse bacterial pathogens, offer unique therapeutic advantages, including their capacity to lyse antibiotic-resistant bacteria and disrupt biofilms without harming the host microbiota. The lack of new effective antibiotics and the growing limitations of existing antibiotics have refocused attention on phage therapy as an option in complex clinical cases such as burn wounds, cystic fibrosis, and pneumonia. This review describes clinical cases and preclinical studies in which phage therapy has been effective in both human and veterinary medicine, and in an agricultural context. In addition, critical challenges, such as the narrow host range of bacteriophages, the possibility of bacterial resistance, and regulatory constraints on the widespread use of phage therapy, are addressed. Future directions include optimizing phage therapy through strategies ranging from phage cocktails to broadening phage host range through genetic modification, and using phages as vaccines or biocontrol agents. In the future, if phage can be efficiently delivered, maintained in a stable state, and phage-antibiotic synergy can be achieved, phage therapy will offer much needed treatment options. However, the successful implementation of phage therapy within the current standards of practice will also require the considerable development of regulatory infrastructure and greater public acceptance. In closing, this review highlights the promise of phage therapy as a critical backup or substitute for antibiotics. It proposes a new role as a significant adjunct to, or even replacement for, antibiotics in treating multidrug-resistant bacterial infections.

PMID:40104270 | PMC:PMC11919290 | DOI:10.1016/j.imj.2025.100168

iScience. 2025 Feb 12;28(3):111999. doi: 10.1016/j.isci.2025.111999. eCollection 2025 Mar 21.

ABSTRACT

Pre-existing respiratory diseases may influence coronavirus disease (COVID-19) susceptibility and severity. However, the molecular mechanisms underlying the airway epithelial response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection severity in patients with chronic respiratory diseases remain unelucidated. Using an in vitro model of differentiated primary bronchial epithelial cells, we aimed to investigate the molecular mechanisms of SARS-CoV-2 infection in pre-existing cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Our study revealed reduced susceptibility of CF and COPD airway epithelia to SARS-CoV-2, relative to that in healthy controls. Mechanistically, reduced transmembrane serine protease 2 (TMPRSS2) activity potentially contributed to this resistance of CF epithelium. Upregulated complement and inflammatory pathways in CF and COPD epithelia potentially primed the antiviral state prior to infection. Analysis of a COVID-19 patient cohort validated our findings, correlating specific inflammatory markers (IP-10, SERPINA1, and CFB) with COVID-19 severity. This study elucidates SARS-CoV-2 pathogenesis and identifies potential biomarkers for clinical monitoring.

PMID:40104058 | PMC:PMC11914195 | DOI:10.1016/j.isci.2025.111999

Sci Rep. 2025 Mar 18;15(1):9264. doi: 10.1038/s41598-025-94184-x.

ABSTRACT

Measuring the quality of life in patients with cystic fibrosis is an important element of the patient care process. Many tools have been created for this measurement among adults. One of them is the Cystic Fibrosis Questionnaire-Revised 14+ (CFQ-R 14+). Its measurement properties have not been comprehensively assessed in the population of Polish adults. The aim of the study is to verify the construct validity, including structural and criterion validity, as well as internal consistency, of the Polish version of the CFQ-R 14+ in the population of adults with cystic fibrosis. We conducted a cross-sectional survey among adults with cystic fibrosis. After preparing the database, we performed a confirmatory factor analysis (CFA) followed by exploratory factor analysis (EFA) using the parallel analysis method principal axis factoring with Oblimin rotation. Intercorrelations of questionnaire factors and the occurrence of relationships among items for the general scale results was checked. We also presented basic descriptive statistics (mean, median, standard deviation, skewness, kurtosis, minimum and maximum values). The analyses included responses from 220 adult patients. CFA results did not show adequate goodness of fit (χ2(1025) = 2112.35, p < 0.001; CFI = 0.831; TLI = 0.814; RMSEA = 0.069; SRMR = 0.074). After EFA, 6 factors were extracted, considering 40 out of 50 questions of the CFQ-R 14+. CFQ-R 14+ may be useful in assessing the quality of life of Polish adult patients with cystic fibrosis. Our analysis demonstrates that the optimal factor structure of the tool in this population includes 6 scales.

PMID:40102545 | DOI:10.1038/s41598-025-94184-x

Lung. 2025 Mar 18;203(1):49. doi: 10.1007/s00408-025-00802-w.

ABSTRACT

PURPOSE: The objective was to study comparative efficacies of cystic fibrosis transmembrane conductance regulator (CFTR) modulators, vanzacaftor-tezacaftor-deutivacaftor (VTD), elexacaftor-tezacaftor-ivacaftor (ETI), tezacaftor-ivacaftor (Tez-Iva), and lumacaftor-ivacaftor (Lum-Iva) in people with cystic fibrosis (pwCF), aged ≥ 12 years, carrying at least one F508del-CFTR-allele.

METHODS: Data from randomized controlled or randomized active comparator trials were included in this network meta-analysis which used frequentist approach for comparing the efficacy of drugs and ranking based on P-scores. Outcomes of interest were mean differences in percentage-predicted forced expiratory volume in one second (ppFEV1), CF questionnaire-revised respiratory domain (CFQ-R) scores, sweat chloride (SwCl) levels, and odds ratios (OR) for serious adverse events (SAE).

RESULTS: Data from 13 studies were analyzed. Compared to placebo, the effects of VTD and ETI on ppFEV1 were almost quadruple of Tez-Iva and Lum-Iva (VTD: 12.78 [95% confidence intervals: 6.41; 19.15] and ETI: 11.95 [7.40; 16.50]) and almost seven times of Tez-Iva and Lum-Iva for CFQ-R (VTD: 21.23 [- 28.72; 71.18] and ETI: 19.27 [10.56; 27.98]). A statistically significant difference was noted between VTD and ETI in SwCl reduction (mean difference: - 8.59 [- 15.53; - 1.65]). There were no statistically significant ORs for SAEs for any CFTR modulators but VTD, ETI, and Tez-Iva were least associated with SAEs (ORs were 0.15 [0.01; 1.79], 0.49 [0.31; 0.78], and 0.74 [0.50; 1.09], respectively, as compared to placebo). Overall, P-score ranking ranked VTD as first and ETI as second, followed by others.

CONCLUSION: VTD and ETI were more efficacious than Tez-Iva and Lum-Iva in pwCF with at least one F508del-CFTR-allele.

PMID:40102290 | DOI:10.1007/s00408-025-00802-w

Dentomaxillofac Radiol. 2025 Mar 18:twaf017. doi: 10.1093/dmfr/twaf017. Online ahead of print.

ABSTRACT

OBJECTIVES: Temporomandibular joint disorder (TMD) patients experience a variety of clinical symptoms, and magnetic resonance imaging (MRI) is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep learning model to generate T2-weighted (T2w) images from proton density-weighted (PDw) images, reducing MRI scan time for TMD patients.

METHODS: A dataset of 7,226 images from 178 patients who underwent TMJ MRI examinations was used. The proposed model employed a generative adversarial network framework with a TransUNet architecture as the generator for image translation. Additionally, a disc segmentation decoder was integrated to improve image quality in the TMJ disc region. The model performance was evaluated using metrics such as the structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). Three experienced oral radiologists also performed a qualitative assessment through the mean opinion score (MOS).

RESULTS: The model demonstrated high performance in generating T2w images from PDw images, achieving average SSIM, LPIPS, and FID values of 82.28%, 2.46, and 23.85, respectively, in the disc region. The model also obtained an average MOS score of 4.58, surpassing other models. Additionally, the model showed robust segmentation capabilities for the TMJ disc.

CONCLUSION: The proposed model using the transformer, complemented by an integrated disc segmentation task, demonstrated strong performance in MR image generation, both quantitatively and qualitatively. This suggests its potential clinical significance in reducing MRI scan times for TMD patients while maintaining high image quality.

PMID:40104864 | DOI:10.1093/dmfr/twaf017

NAR Genom Bioinform. 2025 Mar 18;7(1):lqaf021. doi: 10.1093/nargab/lqaf021. eCollection 2025 Mar.

ABSTRACT

The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Unlike protein structure prediction, RNA structure prediction remains a significant challenge in bioinformatics, primarily due to the scarcity of annotated RNA structure data and its varying quality. Many methods have used this limited data to train deep learning models but redundancy, data leakage and bad data quality hampers their performance. In this work, we present NucleoSeeker, a tool designed to curate high-quality, tailored datasets from the Protein Data Bank (PDB) database. It is a unified framework that combines multiple tools and streamlines an otherwise complicated process of data curation. It offers multiple filters at structure, sequence, and annotation levels, giving researchers full control over data curation. Further, we present several use cases. In particular, we demonstrate how NucleoSeeker allows the creation of a nonredundant RNA structure dataset to assess AlphaFold3's performance for RNA structure prediction. This demonstrates NucleoSeeker's effectiveness in curating valuable nonredundant tailored datasets to both train novel and judge existing methods. NucleoSeeker is very easy to use, highly flexible, and can significantly increase the quality of RNA structure datasets.

PMID:40104673 | PMC:PMC11915511 | DOI:10.1093/nargab/lqaf021

J Pathol Inform. 2025 Feb 4;17:100422. doi: 10.1016/j.jpi.2025.100422. eCollection 2025 Apr.

ABSTRACT

Meningiomas, the most prevalent tumors of the central nervous system, can have overlapping histopathological features with solitary fibrous tumors (SFT), presenting a significant diagnostic challenge. Accurate differentiation between these two diagnoses is crucial for optimal medical management. Currently, immunohistochemistry and molecular techniques are the methods of choice for distinguishing between them; however, these techniques are expensive and not universally available. In this article, we propose a rotational and scale-invariant deep learning framework to enable accurate discrimination between these two tumor types. The proposed framework employs a novel architecture of conical transformers to capture both global and local imaging markers from whole-slide images, accommodating variations across different magnification scales. A weighted majority voting schema is utilized to combine individual scale decisions, ultimately producing a complementary and more accurate diagnostic outcome. A dataset comprising 92 patients (46 with meningioma and 46 with SFT) was used for evaluation. The experimental results demonstrate robust performance across different validation methods. In train-test evaluation, the model achieved 92.27% accuracy, 87.77% sensitivity, 97.55% specificity, and 92.46% F1-score. Performance further improved in 4-fold cross-validation, achieving 94.68% accuracy, 96.05% sensitivity, 93.11% specificity, and 95.07% F1-score. These findings highlight the potential of AI-based diagnostic approaches for precise differentiation between meningioma and SFT, paving the way for innovative diagnostic tools in pathology.

PMID:40104410 | PMC:PMC11914819 | DOI:10.1016/j.jpi.2025.100422

Turk J Med Sci. 2023 Oct 16;55(1):161-177. doi: 10.55730/1300-0144.5954. eCollection 2025.

ABSTRACT

BACKGROUND/AIM: Skin lesions are commonly diagnosed and classified using dermoscopic images. There are many artifacts visible in dermoscopic images, including hair strands, noise, bubbles, blood vessels, poor illumination, and moles. These artifacts can obscure crucial information about lesions, which limits the ability to diagnose lesions automatically. This study investigated how hair and noise artifacts in lesion images affect classifier performance and how they can be removed to improve diagnostic accuracy.

MATERIALS AND METHODS: A synthetic dataset created using hair simulation and noise simulation was used in conjunction with the HAM10000 benchmark dataset. Moreover, integrated convolutional neural networks (CNNs) were proposed for removing hair artifacts using hair inpainting and classification of refined dehaired images, called integrated hair removal (IHR), and for removing noise artifacts using nonlocal mean denoising and classification of refined denoised images, called integrated noise removal (INR).

RESULTS: Five deep learning models were used for the classification: ResNet50, DenseNet121, ResNet152, VGG16, and VGG19. The proposed IHR-DenseNet121, IHR-ResNet50, and IHR-ResNet152 achieved 2.3%, 1.78%, and 1.89% higher accuracy than DenseNet121, ResNet50, and ResNet152, respectively, in removing hairs. The proposed INR-DenseNet121, INR-ResNet50, and INR-VGG19 achieved 1.41%, 2.39%, and 18.4% higher accuracy than DenseNet121, ResNet50, and VGG19, respectively, in removing noise.

CONCLUSION: A significant proportion of pixels within lesion areas are influenced by hair and noise, resulting in reduced classification accuracy. However, the proposed CNNs based on IHR and INR exhibit notably improved performance when restoring pixels affected by hair and noise. The performance outcomes of this proposed approach surpass those of existing methods.

PMID:40104314 | PMC:PMC11913500 | DOI:10.55730/1300-0144.5954

Turk J Med Sci. 2024 Aug 6;55(1):140-151. doi: 10.55730/1300-0144.5952. eCollection 2025.

ABSTRACT

BACKGROUND/AIM: Motor neuron disease (MND) is a devastating neuron ailment that affects the motor neurons that regulate muscular voluntary actions. It is a rare disorder that gradually destroys aspects of neurological function. In general, MND arises as a result of a combination of natural, behavioral, and genetic influences. However, early detection of MND is a challenging task and manual identification is time-consuming. To overcome this, a novel deep learning-based duple feature extraction framework is proposed for the early detection of MND.

MATERIALS AND METHODS: Diffusion tensor imaging tractography (DTI) images were initially analyzed for color and textural features using dual feature extraction. Local binary pattern (LBP)-based methods were used to extract textural data from images by examining nearby pixel values. A color information feature was then added to the LBP-based feature during the classification phase for extracting color features. A flattened image was then fed into the MONDNet for classifying normal and abnormal cases of MND based on color and texture features.

RESULTS: The proposed deep MONDNet is suitable because it achieved a detection rate of 99.66% and can identify MND in its early stages.

CONCLUSION: The proposed mobile net model achieved an overall F1 score of 13.26%, 6.15%, 5.56%, and 5.96% compared to the BPNN, CNN, SVM-RFE, and MLP algorithms, respectively.

PMID:40104302 | PMC:PMC11913516 | DOI:10.55730/1300-0144.5952

Phys Imaging Radiat Oncol. 2025 Feb 21;33:100736. doi: 10.1016/j.phro.2025.100736. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: Delineation of target volumes (TVs) and organs at risk (OARs) is a resource intensive process in lung radiation therapy and, despite the introduction of some auto-contouring, inter-observer variability remains a challenge. Deep learning algorithms may prove an efficient alternative and this review aims to map the evidence base on the use of deep learning algorithms for TV and OAR delineation in the radiation therapy planning process for lung cancer patients.

MATERIALS AND METHODS: A literature search identified studies relating to deep learning. Manual contouring and deep learning auto-contours were evaluated against one another for accuracy, inter-observer variability, contouring time and dose-volume effects. A total of 40 studies were included for review.

RESULTS: Thirty nine out of 40 studies investigated the accuracy of deep learning auto-contours and determined that they were of a comparable accuracy to manual contours. Inter-observer variability outcomes were heterogeneous in the seven relevant studies identified. Twenty-four studies analysed the time saving associated with deep learning auto-contours and reported a significant time reduction in comparison to manual contours. The eight studies that conducted a dose-volume metric evaluation on deep learning auto-contours identified negligible effect on treatment plans.

CONCLUSION: The accuracy and time-saving capacity of deep learning auto-contours in comparison to manual contours has been extensively studied. However, additional research is required in the areas of inter-observer variability and dose-volume metric evaluation to further substantiate its clinical use.

PMID:40104215 | PMC:PMC11914827 | DOI:10.1016/j.phro.2025.100736

Ophthalmol Sci. 2025 Jan 28;5(3):100719. doi: 10.1016/j.xops.2025.100719. eCollection 2025 May-Jun.

NO ABSTRACT

PMID:40103835 | PMC:PMC11914739 | DOI:10.1016/j.xops.2025.100719

Front Med (Lausanne). 2025 Mar 4;12:1553970. doi: 10.3389/fmed.2025.1553970. eCollection 2025.

ABSTRACT

BACKGROUND: Critical illness medicine faces challenges such as high data complexity, large individual differences, and rapid changes in conditions. Artificial Intelligence (AI) technology, especially machine learning and deep learning, offers new possibilities for addressing these issues. By analyzing large amounts of patient data, AI can help identify diseases earlier, predict disease progression, and support clinical decision-making.

METHODS: In this study, scientific literature databases such as Web of Science were searched, and bibliometric methods along with visualization tools R-bibliometrix, VOSviewer 1.6.19, and CiteSpace 6.2.R4 were used to perform a visual analysis of the retrieved data.

RESULTS: This study analyzed 900 articles from 6,653 authors in 82 countries between 2005 and 2024. The United States is a major contributor in this field, with Harvard University having the highest betweenness centrality. Noseworthy PA is a core author in this field, and Frontiers in Cardiovascular Medicine and Diagnostics lead other journals in terms of the number of publications. Artificial Intelligence has tremendous potential in the identification and management of heart failure and sepsis.

CONCLUSION: The application of AI in critical illness holds great potential, particularly in enhancing diagnostic accuracy, personalized treatment, and clinical decision support. However, to achieve widespread application of AI technology in clinical practice, challenges such as data privacy, model interpretability, and ethical issues need to be addressed. Future research should focus on the transparency, interpretability, and clinical validation of AI models to ensure their effectiveness and safety in critical illness.

PMID:40103796 | PMC:PMC11914116 | DOI:10.3389/fmed.2025.1553970

Data Brief. 2025 Feb 12;59:111379. doi: 10.1016/j.dib.2025.111379. eCollection 2025 Apr.

ABSTRACT

The economic development of many countries largely depends on tea plantations that suffer from diseases adversely affecting their productivity and quality. This study presents a high-resolution dataset aimed at advancing precision agriculture for managing tea garden diseases. The size of the dataset is 3960 images and pixel dimension is (1024 × 1024) of the images were collected by using smartphones. This dataset contains detailed images of Tea Leaf Blight, Tea Red Leaf Spot and Tea Red Scab maladies inflicted on tea leaves as well as environmental statistics and plant health. The images were captured and stored in JPG format. The main aim of this dataset is to provide tool for detection and classification of different types of tea garden disease. Applying this dataset will enable the development of early detection systems, best-practice care regimens, and enhanced general garden upkeep. A range of images presenting the most prevalent diseases afflicting tea plants are paired with images of healthy leaves to provide a comprehensive overview of all the circumstances that can arise in a tea plantation. Therefore, it can be used to automate diseases tracking, targeted pesticide spraying, and even the making of smart farm tools with development of smart agricultural tools hence enhancing sustainability and efficiency in tea production. This dataset not only provides a strong foundation for applying precision techniques in tea cultivation in agriculture, but also can become an invaluable asset to scientists studying the issues of tea production.

PMID:40103762 | PMC:PMC11914274 | DOI:10.1016/j.dib.2025.111379

Data Brief. 2025 Feb 12;59:111387. doi: 10.1016/j.dib.2025.111387. eCollection 2025 Apr.

ABSTRACT

The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researchers proposed various datasets of radio signals of different types of devices (drones, cell phones, IoT, and Radar). However, presently there is no freely available dataset on walkie-talkies/commercial radios. To fill out the void, we present an innovative dataset including more than 2700 radio signals captured from 27 radios located in an indoor multipath environment. This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.

PMID:40103755 | PMC:PMC11914181 | DOI:10.1016/j.dib.2025.111387

Respir Med Case Rep. 2025 Feb 27;54:102184. doi: 10.1016/j.rmcr.2025.102184. eCollection 2025.

ABSTRACT

An 81-year-old man with a history of interstitial lung disease attributed to idiopathic pulmonary fibrosis, severe aortic stenosis, and stable coronary artery disease was started on inhaled treprostinil for pulmonary hypertension associated with interstitial lung disease to optimize hemodynamics prior to the valve replacement procedure. However, two days after starting this treatment, the patient presented with an inferior-posterior ST elevation myocardial infarction. Urgent coronary angiography revealed a 95 % proximal right coronary artery stenosis, successfully treated with percutaneous coronary intervention and drug-eluting stent placement. This case raises a question of whether there could be a potential association between inhaled treprostinil initiation and acute myocardial infarction in patients with underlying coronary artery disease. With the documented stability of the patient's coronary artery disease prior to medication initiation, it is plausible to question whether treprostinil may have played a role in plaque destabilization.

PMID:40104432 | PMC:PMC11915154 | DOI:10.1016/j.rmcr.2025.102184

Nat Rev Drug Discov. 2025 Mar 18. doi: 10.1038/s41573-025-01158-9. Online ahead of print.

ABSTRACT

Fibrosis is a pathophysiological mechanism involved in chronic and progressive diseases that results in excessive tissue scarring. Diseases associated with fibrosis include metabolic dysfunction-associated steatohepatitis (MASH), inflammatory bowel diseases (IBDs), chronic kidney disease (CKD), idiopathic pulmonary fibrosis (IPF) and systemic sclerosis (SSc), which are collectively responsible for substantial morbidity and mortality. Although a few drugs with direct antifibrotic activity are approved for pulmonary fibrosis and considerable progress has been made in the understanding of mechanisms of fibrosis, translation of this knowledge into effective therapies continues to be limited and challenging. With the aim of assisting developers of novel antifibrotic drugs, this Review integrates viewpoints of biologists and physician-scientists on core pathways involved in fibrosis across organs, as well as on specific characteristics and approaches to assess therapeutic interventions for fibrotic diseases of the lung, gut, kidney, skin and liver. This discussion is used as a basis to propose strategies to improve the translation of potential antifibrotic therapies.

PMID:40102636 | DOI:10.1038/s41573-025-01158-9

J Mol Cell Cardiol Plus. 2025 Feb 19;11:100289. doi: 10.1016/j.jmccpl.2025.100289. eCollection 2025 Mar.

ABSTRACT

The regulatory mechanisms underlying the response to pro-inflammatory cytokines in cardiac diseases are poorly understood. Here, we use iPSC-derived cardiovascular progenitor cells (CVPCs) to model the response to interferon gamma (IFNγ) in human cardiac tissue. We generate RNA-seq and ATAC-seq for four CVPCs that were treated with IFNγ and compare them with paired untreated controls. Transcriptional differences after treatment show that IFNγ initiates an innate immune cell-like response, shifts the CVPC transcriptome toward coronary artery and aorta profiles, and stimulates expression of endothelial cell-specific genes. Analysis of the accessible chromatin shows that IFNγ is a potent chromatin remodeler and establishes an IRF-STAT immune-cell like regulatory network. Finally, we show that 11 GWAS risk variants for 8 common cardiac diseases overlap IFNγ-upregulated ATAC-seq peaks. Our findings reveal insights into IFNγ-induced activation of an immune-like regulatory network in human cardiac tissue and the potential role that regulatory elements in this pathway play in common cardiac diseases.

PMID:40104808 | PMC:PMC11919396 | DOI:10.1016/j.jmccpl.2025.100289

World J Transplant. 2025 Mar 18;15(1):96025. doi: 10.5500/wjt.v15.i1.96025.

ABSTRACT

BACKGROUND: Despite the developments in the field of kidney transplantation, the already existing diagnostic techniques for patient monitoring are considered insufficient. Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies (serum, urine) represent a promising innovation in the monitoring of kidney transplant recipients.

AIM: To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.

METHODS: A systematic review was conducted in accordance with PRISMA guidelines, based on research results from the PubMed and Scopus databases. The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related complications. Eligibility criteria included protein biomarkers and urine and blood samples, while exclusion criteria were language other than English and the use of low resolution and sensitivity methods. The selected research articles, were categorized based on the biological sample, condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted. Functional and network analysis of the selected proteins was performed.

RESULTS: In 17 included studies, 58 proteins were studied, with the cytokine CXCL10 being the most investigated. Biological pathways related to immune response and fibrosis have shown to be enriched. Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported. Overall, all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods, as far as renal graft assessment is concerned.

CONCLUSION: Our review suggests that protein biomarkers, evaluated in specific biological fluids, can make a significant contribution to the timely, valid and non-invasive assessment of kidney graft.

PMID:40104186 | PMC:PMC11612886 | DOI:10.5500/wjt.v15.i1.96025