RSC Adv. 2025 Jan 23;15(4):2298-2316. doi: 10.1039/d4ra08245a. eCollection 2025 Jan 23.

ABSTRACT

Despite significant strides in improving cancer survival rates, the global cancer burden remains substantial, with an anticipated rise in new cases. Immune checkpoints, key regulators of immune responses, play a crucial role in cancer evasion mechanisms. The discovery of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 has revolutionized cancer treatment, with monoclonal antibodies (mAbs) becoming widely prescribed. However, challenges with current mAb ICIs, such as limited oral bioavailability, adverse effects, and high costs, underscore the need to explore alternative small-molecule inhibitors. In this work, we aimed to identify new potential ICI among all FDA-approved drugs. We employed QSAR models to predict PD-1/PD-L1 inhibition, utilizing a diverse dataset of 29 197 molecules sourced from ChEMBL, PubChem, and recent literature. Machine learning techniques, including Random Forest, Support Vector Machine, and Convolutional Neural Network, were employed for benchmarking to assess model performance. Additionally, we undertook a drug repurposing strategy, leveraging the best in silico model for a virtual screening campaign involving 1576 off-patent approved drugs. Only two virtual screening hits were proposed based on the criteria established for this approach, including: (1) QSAR probability of being active against PD-L1; (2) QSAR applicability domain; (3) prediction of the affinity between the PD-L1 and ligands through molecular docking. One of the proposed hits was sonidegib, an anticancer drug, featuring a biphenyl system. Sonidegib was subsequently validated for in vitro PD-1/PD-L1 binding modulation using ELISA and flow cytometry. This integrated approach, which combines computer-aided drug design (CADD) tools, QSAR modelling, drug repurposing, and molecular docking, offers a pioneering strategy to expedite drug discovery for PD-1/PD-L1 axis inhibition. The findings underscore the potential to identify a wider range small molecules to contribute to the ongoing efforts to advancing cancer immunotherapy.

PMID:39867321 | PMC:PMC11755407 | DOI:10.1039/d4ra08245a

ACS Omega. 2025 Jan 8;10(2):1864-1892. doi: 10.1021/acsomega.4c09114. eCollection 2025 Jan 21.

ABSTRACT

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It occurs due to a gradual deficit of dopamine-producing brain cells, particularly in the substantia nigra. The precise etiology of PD is not fully understood, but it likely involves a combination of genetic and environmental factors. The therapies available at present alleviate symptoms but do not stop the disease's advancement. Research endeavors are currently directed at inventing disease-controlling therapies that aim at the inherent mechanisms of PD. PD biomarker breakthroughs hold enormous potential: earlier diagnosis, better monitoring, and targeted treatment based on individual response could significantly improve patient outcomes and ease the burden of this disease. PD research is an active and evolving field, focusing on understanding disease mechanisms, identifying biomarkers, developing new treatments, and improving care. In this report, we explore data from the CAS Content Collection to outline the research progress in PD. We analyze the publication landscape to offer perspective into the latest expertise advancements. Key emerging concepts are reviewed and strategies to fight disease evaluated. Pharmacological targets, genetic risk factors, as well as comorbid diseases are explored, and clinical usage of products against PD with their production pipelines and trials for drug repurposing are examined. This review aims to offer a comprehensive overview of the advancing landscape of the current understanding about PD, to define challenges, and to assess growth prospects to stimulate efforts in battling the disease.

PMID:39866628 | PMC:PMC11755173 | DOI:10.1021/acsomega.4c09114

Heliyon. 2025 Jan 3;11(1):e41688. doi: 10.1016/j.heliyon.2025.e41688. eCollection 2025 Jan 15.

ABSTRACT

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and "response to radiation" as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing common pathways and potential therapeutic targets for developing novel treatment strategies and repurposing existing drugs for these prevalent skin cancers. Therefore, these findings may guide future research in understanding and developing targeted therapies for both conditions.

PMID:39866430 | PMC:PMC11761328 | DOI:10.1016/j.heliyon.2025.e41688

Front Mol Neurosci. 2025 Jan 10;17:1503396. doi: 10.3389/fnmol.2024.1503396. eCollection 2024.

ABSTRACT

Multiple sclerosis (MS) affects 2.8 million people worldwide. Although the cause is unknown, various risk factors might be involved. MS involves the immune system attacking the central nervous system's myelin sheath, leading to neuron damage. This study used a cuprizone (CPZ)-intoxicated mouse model to simulate MS's demyelination/remyelination process. It evaluated the molecular, histological, and behavioral effects of vanillic acid (VA), a natural phenolic acid, alone and with Ibudilast (IBD), a clinically tested MS medication. Mice were divided into a control group (regular chow) and a CPZ group (0.3% cuprizone chow for 5 consecutive weeks). During remyelination, the CPZ group was split into four groups: no therapy, 10 mg/kg of IBD, 30 mg/kg of VA, and combined, each treated for 4 weeks. Behavioral, biochemical, molecular, and histopathological tests occurred in the 5th week (demyelination), 7th (early remyelination), and 9th (late remyelination). Cognitive assessments were at weeks 5 and 9. VA enhanced motor, coordination, and cognitive impairments in CPZ-intoxicated mice and improved histopathological, molecular, and biochemical features during early remyelination. IBD improved behavioral abnormalities across all tests, but combined therapy showed no significant difference from single therapies. Further investigations are necessary to understand VA's mechanisms and potential as an MS treatment.

PMID:39866908 | PMC:PMC11760597 | DOI:10.3389/fnmol.2024.1503396

Curr Neuropharmacol. 2025 Jan 24. doi: 10.2174/011570159X349579241231080602. Online ahead of print.

ABSTRACT

INTRODUCTION: Glucagon-Like Peptide-1 Receptor (GLP1R) agonists have become widespread anti-obesity/diabetes pharmaceuticals in the United States.

AIM: This article aimed to provide our current knowledge on the plausible mechanisms linked to the role of Ozempic (Semaglutide), which is generalized as one of the anti-addiction compounds.

METHODS: The effects of GLP1R agonists in Alcohol Use Disorder (AUD) and substance use disorder (SUD) are mediated, in part, through the downregulation of dopamine signaling. We posit that while GLP1R agonism could offer therapeutic advantages in hyperdopaminergia, it may be detrimental in patients with hypodopaminergia, potentially leading to long-term induction of Suicidal Ideation (SI). The alleged posit of GLP1 agonists to induce dopamine homeostasis is incorrect. This study refined 31 genes based on the targets of Ozempic, GLP1R, and related enzymes for SI and 10 genes of the Genetic Addiction Risk Score (GARS) test. STRING-MODEL refined 29 genes, and further primary analyses indicated associations of GLP1R with DRD3, BDNF, CREB1, CRH, IL6, and DPP4.

RESULTS: In-depth silico enrichment analysis revealed an association between candidate genes and depressive phenotypes linked with dopaminergic signaling. Finally, through primary and in-depth silico analyses, we demonstrated multiple findings supporting that GLP1R agonists can induce depression phenotypes.

CONCLUSION: Our findings suggest that associated polymorphisms seem to have overlapping effects with addictive behaviors of Reward Deficiency Syndrome (RDS) and dopamine regulation. Consequently, GLP1R agonists may represent a double-edged sword, potentially triggering both antiaddictive effects and SI by exacerbating depressive phenotypes. Thus, we encourage the scientific community to perform further empirical clinical studies to confirm this proposed pathway.

PMID:39865816 | DOI:10.2174/011570159X349579241231080602

Biofilm. 2024 Dec 30;9:100252. doi: 10.1016/j.bioflm.2024.100252. eCollection 2025 Jun.

ABSTRACT

Antibiotics are central to managing airway infections in cystic fibrosis (CF), yet current treatments often fail due to the presence of Pseudomonas aeruginosa biofilms, settling down the need for seeking therapies targeting biofilms. This study aimed to investigate the antibiofilm activity of aspartic acid and its potential as an adjuvant to tobramycin against P. aeruginosa biofilms formed by mucoid and small colony variant (SCV) tobramycin tolerant strain. We assessed the effect of aspartic acid on both surface-attached and suspended P. aeruginosa biofilms within CF artificial mucus and investigated the synergistic impact of combining it with non-lethal tobramycin concentrations. Our findings showed that aspartic acid inhibited planktonic P. aeruginosa without affecting its viability and prevented biofilm formation by hindering bacterial adhesion or interfering with EPS production, depending on the experimental conditions. In CF mucus, aspartic acid significantly reduced bacterial growth, with the highest inhibition observed when combined with tobramycin, showing notable effects against the mucoid and tolerant SCV strain. Despite these reductions, P. aeruginosa repopulated the mucus within 24 h of stress withdrawal. Additional strategies, including delayed tobramycin application and a second dose of co-application of aspartic acid and tobramycin were explored to address bacterial survival and recovery. Although none of the strategies eradicated P. aeruginosa, the second co-application resulted in slower bacterial recovery rates. In conclusion, this study highlighted aspartic acid as an effective antibiofilm agent and demonstrated for the first time its potential as an adjuvant to tobramycin. The combined use of aspartic acid and tobramycin offers a promising advancement in CF therapeutics, particularly against P. aeruginosa biofilms formed by mucoid and SCV strains, mitigating their antibiotic resistance.

PMID:39866543 | PMC:PMC11759549 | DOI:10.1016/j.bioflm.2024.100252

Heliyon. 2025 Jan 7;11(1):e41716. doi: 10.1016/j.heliyon.2025.e41716. eCollection 2025 Jan 15.

ABSTRACT

BACKGROUND: Cystic fibrosis is a heterogeneous disease whose severity and symptoms largely depend on the functional impact of mutations in the cystic fibrosis transmembrane conductance regulator gene. Other genes may also modulate the clinical manifestations and complications associated with cystic fibrosis. Genetic variants of the bitter taste receptor TAS2R38 have been shown to contribute to the susceptibility and severity of chronic rhinosinusitis. This study aims to elucidate the role of TAS2R38 as a novel modifier gene influencing sinonasal disease severity and pulmonary Pseudomonas Aeruginosa colonization in children with cystic fibrosis.

METHODS: This retrospective observational case-control study evaluated sinus clinical features, quality of life, and the occurrence of Pseudomonas Aeruginosa pulmonary colonization in 69 children with cystic fibrosis. Propylthiouracil testing and TAS2R38 genotyping were performed to characterize patients based on receptor functionality.

RESULTS: The non-taster genetic variant of bitter taste receptor TAS2R38 was associated with greater severity of chronic rhinosinusitis, as measured by endoscopic and radiological scores, compared to the taster variant (p = 0.031 and p = 0.03, respectively). Furthermore, an inverse correlation was observed between the age at first Pseudomonas Aeruginosa infection and chronic rhinosinusitis severity assessed by endoscopic score (r = -0.3388, p = 0.0302).

CONCLUSIONS: The findings highlight the role of TAS2R38 as a potential genetic modifier influencing the severity of chronic rhinosinusitis in children with cystic fibrosis. The clinical implications include the potential development of T2R38-targeted topical therapies and the use of taste testing or genotyping to predict susceptibility to infection. In addition, these results may pave the way for novel, tailored therapeutic approaches in the era of precision medicine.

PMID:39866409 | PMC:PMC11761314 | DOI:10.1016/j.heliyon.2025.e41716

iScience. 2024 Dec 20;28(1):111658. doi: 10.1016/j.isci.2024.111658. eCollection 2025 Jan 17.

ABSTRACT

The regulation of gene expression relies on the coordinated action of transcription factors (TFs) at enhancers, including both activator and repressor TFs. We employed deep learning (DL) to dissect HepG2 enhancers into positive (PAR), negative (NAR), and neutral activity regions. Sharpr-MPRA and STARR-seq highlight the dichotomy impact of NARs and PARs on modulating and catalyzing the activity of enhancers, respectively. Approximately 22% of HepG2 enhancers, termed "repressive impact enhancers" (RIEs), are predominantly populated by NARs and transcriptional repression motifs. Genes flanking RIEs exhibit a stage-specific decline in expression during late development, suggesting RIEs' role in trimming enhancer activities. About 16.7% of human NARs emerge from neutral rhesus macaque DNA. This gain of repressor binding sites in RIEs is associated with a 30% decrease in the average expression of flanking genes in humans compared to rhesus macaque. Our work reveals modulated enhancer activity and adaptable gene regulation through the evolutionary dynamics of TF binding sites.

PMID:39868043 | PMC:PMC11761325 | DOI:10.1016/j.isci.2024.111658

iScience. 2024 Dec 20;28(1):111638. doi: 10.1016/j.isci.2024.111638. eCollection 2025 Jan 17.

ABSTRACT

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNA-seq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

PMID:39868035 | PMC:PMC11758823 | DOI:10.1016/j.isci.2024.111638

Pak J Med Sci. 2025 Jan;41(1):225-229. doi: 10.12669/pjms.41.1.9171.

ABSTRACT

OBJECTIVE: To design a deep learning-based model for early screening of diabetic retinopathy, predict the condition, and provide interpretable justifications.

METHODS: The experiment's model structure is designed based on the Vision Transformer architecture which was initiated in March 2023 and the first version was produced in July 2023 at Affiliated Hospital of Hangzhou Normal University. We use the publicly available EyePACS dataset as input to train the model. Using the trained model, we predict whether a given patient's fundus images indicate diabetic retinopathy and provide the relevant affected areas as the basis for the judgement.

RESULTS: The model was validated using two subsets of the IDRiD dataset. Our model not only achieved good results in terms of detection accuracy, reaching around 0.88, but also performed comparably to similar models annotated for affected areas in predicting the affected regions.

CONCLUSION: Utilizing image-level annotations, we implemented a method for detecting diabetic retinopathy through deep learning and provided interpretable justifications to assist clinicians in diagnosis.

PMID:39867796 | PMC:PMC11755306 | DOI:10.12669/pjms.41.1.9171

J Pathol Inform. 2024 Dec 11;16:100416. doi: 10.1016/j.jpi.2024.100416. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly supervised learning strategies showing promising results even with limited manual annotation.

PURPOSE: To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.

METHODS: This is an ethics board approved retrospective study utilizing 217 trichrome WSI from pediatric liver biopsies for model development and testing. Two pediatric pathologists scored WSI using two liver fibrosis staging systems, METAVIR and Ishak. Cases were then secondarily categorized into either high- or low-stage liver fibrosis and used for model development. The CLAM pipeline was used to develop binary classification models for histological liver fibrosis. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and Cohen's Kappa.

RESULTS: The CLAM models showed strong diagnostic performance, with sensitivities up to 0.76 and AUCs up to 0.92 for distinguishing low- and high-stage fibrosis. The agreement between model predictions and average pathologist scores was moderate to substantial (Kappa: 0.57-0.69), whereas pathologist agreement on the METAVIR and Ishak scoring systems was only fair (Kappa: 0.39-0.46).

CONCLUSIONS: CLAM pipeline showed promise in detecting features important for differentiating low- and high-stage fibrosis from trichrome WSI based on the results, offering a promising objective method for liver fibrosis detection in children and young adults.

PMID:39867463 | PMC:PMC11760786 | DOI:10.1016/j.jpi.2024.100416

Front Neurosci. 2025 Jan 10;18:1434444. doi: 10.3389/fnins.2024.1434444. eCollection 2024.

ABSTRACT

INTRODUCTION: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.

METHODS: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks. To address the cross-subject variability in EEG data, we also investigate persistent homology features that are robust to different types of noise. The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features.

RESULTS: The raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features.

DISCUSSION: The findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data.

PMID:39867449 | PMC:PMC11758281 | DOI:10.3389/fnins.2024.1434444

ArXiv [Preprint]. 2025 Jan 10:arXiv:2501.05945v1.

ABSTRACT

Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built on these foundation models remain largely unavailable, hindering their broader utility and impact. To address this gap, we developed SpinPath, a toolkit designed to democratize specimen-level deep learning by providing a zoo of pretrained specimen-level models, a Python-based inference engine, and a JavaScript-based inference platform. We demonstrate the utility of SpinPath in metastasis detection tasks across nine foundation models. SpinPath may foster reproducibility, simplify experimentation, and accelerate the adoption of specimen-level deep learning in computational pathology research.

PMID:39867428 | PMC:PMC11759856

Curr Res Struct Biol. 2025 Jan 3;9:100163. doi: 10.1016/j.crstbi.2024.100163. eCollection 2025 Jun.

ABSTRACT

The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. Due to the lack of protein crystal structure data, about three-quarters of human mutant proteins cannot be predicted or accurately predicted, and the pathogenicity of missense mutations can only be indirectly evaluated by evolutionary conservation. Recently, many computational methods have been developed to predict protein 3D structures with accuracy comparable to experiments. This progress enables the information of structural biology to be further utilized by clinicians. Thus, we developed a user-friendly platform named DPL3D (http://nsbio.tech:3000) which can predict and visualize the 3D structure of mutant proteins. The crystal structure and other information of proteins were downloaded together with the software including AlphaFold 2, RoseTTAFold, RoseTTAFold All-Atom, and trRosettaX-Single. We implemented a query module for 210,180 molecular structures, including 52,248 human proteins. Visualization of protein two-dimensional (2D) and 3D structure prediction can be generated via LiteMol automatically or manually and interactively. This platform will allow users to easily and quickly retrieve large-scale structural information for biological discovery.

PMID:39867105 | PMC:PMC11761317 | DOI:10.1016/j.crstbi.2024.100163

Cureus. 2024 Dec 26;16(12):e76428. doi: 10.7759/cureus.76428. eCollection 2024 Dec.

ABSTRACT

Recent advancements in artificial intelligence (AI) have shown significant potential in the medical field, although many applications are still in the research phase. This paper provides a comprehensive review of advancements in augmented reality (AR), mixed reality (MR), and virtual reality (VR) for surgical applications from 2019 to 2024 to accelerate the transition of AI from the research to the clinical phase. This paper also provides an overview of proposed databases for further use in extended reality (XR), which includes AR, MR, and VR, as well as a summary of typical research applications involving XR in surgical practices. Additionally, this paper concludes by discussing challenges and proposed solutions for the application of XR in the medical field. Although the areas of focus and specific implementations vary among AR, MR, and VR, current trends in XR focus mainly on reducing workload and minimizing surgical errors through navigation, training, and machine learning-based visualization. Through analyzing these trends, AR and MR have greater advantages for intraoperative surgical functions, whereas VR is limited to preoperative training and surgical preparation. VR faces additional limitations, and its use has been reduced in research since the first applications of XR, which likely suggests the same will happen with further development. Nonetheless, with increased access to technology and the ability to overcome the black box problem, XR's applications in medical fields and surgery will increase to guarantee further accuracy and precision while reducing risk and workload.

PMID:39867005 | PMC:PMC11763273 | DOI:10.7759/cureus.76428

Digit Health. 2025 Jan 24;11:20552076251314546. doi: 10.1177/20552076251314546. eCollection 2025 Jan-Dec.

ABSTRACT

PURPOSE: Prostate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland. An automated system is required to assist the medical professionals in accurate and early diagnosis with less effort.

METHOD: This study recognizes the impact of zonal features on the advancement of the disease. The aim is to improve the diagnostic performance through a novel automated approach of a two-step mechanism using bpMRI images. First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. Secondly, pretraining the same type of Attention U-Net is performed for lesion segmentation.

RESULTS: The performance of the pretrained models and training an attention-guided U-Net from the scratch for segmenting tumors on the prostate region is analyzed. The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models.

CONCLUSION: Our approach greatly enhances the identification and categorization of clinically significant PCa by including zonal data. Our approach exhibits exceptional performance in the accurate segmentation of bpMRI images compared to current techniques, as evidenced by thorough validation of a diverse dataset. This research not only enhances the field of medical imaging for oncology but also underscores the potential of deep learning models to progress PCa diagnosis and personalized patient care.

PMID:39866889 | PMC:PMC11758924 | DOI:10.1177/20552076251314546

Res Sq [Preprint]. 2025 Jan 9:rs.3.rs-5776937. doi: 10.21203/rs.3.rs-5776937/v1.

ABSTRACT

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/ and can be utilized for all organisms.

PMID:39866874 | PMC:PMC11760241 | DOI:10.21203/rs.3.rs-5776937/v1

Respir Med Case Rep. 2025 Jan 2;53:102165. doi: 10.1016/j.rmcr.2025.102165. eCollection 2025.

ABSTRACT

BACKGROUND: Anti-Ku antibodies are autoantibodies directed against the Ku protein complex involved in DNA repair. They are typically associated with overlap syndromes featuring polymyositis and systemic sclerosis. Isolated pulmonary involvement without myositis is exceedingly rare.

CASE PRESENTATION: We report the case of a 70-year-old male, a former smoker with an 18-year smoking history who quit 20 years ago, presenting with a one-year history of progressive dyspnea and dry cough. High-resolution computed tomography (HRCT) revealed pulmonary fibrosis with areas of ground-glass opacities. Laboratory tests showed antinuclear antibodies at a titer of 1:2560 with a speckled pattern and positivity for anti-Ku antibodies. Creatine phosphokinase levels were within normal limits. There were no clinical signs of myositis, myalgia, skin manifestations, or Raynaud's phenomenon.

CONCLUSION: This case underscores the rarity of pulmonary fibrosis as the sole clinical manifestation associated with anti-Ku antibody positivity in the absence of myositis. Clinicians should consider testing for anti-Ku antibodies in patients with idiopathic interstitial lung disease, even when muscular and cutaneous symptoms are absent.

PMID:39867941 | PMC:PMC11759561 | DOI:10.1016/j.rmcr.2025.102165

Cureus. 2024 Dec 27;16(12):e76454. doi: 10.7759/cureus.76454. eCollection 2024 Dec.

ABSTRACT

Background Interstitial lung diseases (ILDs) are a group of non-infectious diseases characterized by interstitial inflammation and fibrosis on histological examination. Gastroesophageal reflux disease (GERD) is common in this patient population, but whether there is a causal or coincidental relationship is not yet clear. It still remains unsettled how to diagnose GERD, and the role of different treatment modalities for GERD, in these lung disorders. Furthermore, most of the work is done to find the association of GERD with idiopathic pulmonary fibrosis (IPF) only. The aim of this study was to determine the prevalence of GERD in ILD patients presenting to an ILD clinic. Methods Prospective study of registered ILD patients during a period of eight months (May-December 2016). Diagnosis of GERD was made on a clinical basis (presentation with typical symptoms of heartburn and regurgitation). Current use of acid-reducing medications and steroids was also recorded. Results A total of 79 patients were included in the study. Females (58, 73.41%) outnumbered males (21, 26.58%). The heaviest burden of ILD was contributed by IPF (32, 40.50%), followed by non-specific interstitial pneumonia (NSIP) (26, 32.91%), hypersensitivity pneumonitis (HP) (8, 10.12%), sarcoidosis (5, 6.3%), silicosis (3, 3.8%), desquamative interstitial pneumonia (DIP) (2, 2.5%), and Langerhans cell histiocytosis (LCH) (3, 3.79%). Fifty (63.29%) patients were on steroids, and 29 (36.70%) were already taking anti-reflux medications at presentation. GERD was reported in 21 (65.6%) IPF, 12 (46.15%) NSIP, one (12.5%) HP, one (33.3%) silicosis, two (40%) sarcoidosis, and all (2,100%) of DIP patients. The overall prevalence of GERD was 39 (49.36%) in ILD patients. Conclusion The prevalence of abnormal acid reflux in ILD patients is high. It may be one of the underlying etiologies of lung fibrosis. Long-term follow-up is necessary to determine if control of reflux alters the natural history of these lung disorders. GERD must be investigated and managed optimally for patients with ILD.

PMID:39866988 | PMC:PMC11769694 | DOI:10.7759/cureus.76454

Heliyon. 2025 Jan 2;11(1):e41628. doi: 10.1016/j.heliyon.2025.e41628. eCollection 2025 Jan 15.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. It is characterized by inflammation and fibrosis in the lung parenchyma and interstitium. Given its poor prognosis and limited treatment options, understanding the underlying molecular mechanisms is crucial. Recent evidence suggests that lipid metabolism plays a pivotal role in IPF pathogenesis, however, the precise mechanisms remain poorly understood. To address this, we analyzed 12 bulk RNA-seq and 2 single-cell RNA-seq datasets from the GEO database using machine learning approaches. As a result, we identified four key lipid-related genes-PPARG, SPP1, CASP3, and PECAM1-that are expressed across various cell types. Specifically, in alveolar macrophages (AMs), we observed that PPARG was significantly downregulated, while SPP1 was highly expressed. Importantly, PPARG serves as a transcriptional regulator of SPP1, which in turn mediates intercellular signaling via CD44. Based on these findings, we propose a novel PPARG/SPP1/CD44 signaling pathway in AMs, which modulates lipid metabolism and likely contributes to the progression of fibrosis in IPF. Moreover, network pharmacology analysis identified several herbal compounds that target PPARG, offering potential therapeutic opportunities. In conclusion, these findings highlight the critical role of lipid metabolism in IPF and present novel molecular targets for the development of future therapeutic strategies.

PMID:39866448 | PMC:PMC11761845 | DOI:10.1016/j.heliyon.2025.e41628