Expert Opin Drug Saf. 2025 Apr 12. doi: 10.1080/14740338.2025.2493781. Online ahead of print.

ABSTRACT

BACKGROUND: Guillain-Barré Syndrome (GBS) is a rare but severe neurological disorder often precipitated by infections, vaccines, and potentially by certain medications. Drug-induced GBS, though less commonly reported, presents significant diagnostic and therapeutic challenges. This study investigates the correlation between various medications and the onset of GBS.

RESEARCH DESIGN AND METHODS: We conducted a retrospective pharmacovigilance analysis using data from the FDA Adverse Event Reporting System (FAERS) from Q1 2004 to Q1 2024. The analysis focused on identifying drugs frequently associated with GBS and examining the time-to-onset and severity of these events.

RESULTS: From over 17 million adverse events, 1,869 cases were identified as drug-induced GBS. Monoclonal antibodies and immunomodulators were the most frequently implicated drug classes. The median time-to-onset for GBS was within the first 30 days following drug exposure. Approximately 51.8% of the cases resulted in severe outcomes, such as hospitalization or disability. Drugs like brentuximab vedotin and efalizumab showed strong associations with GBS occurrences.

CONCLUSIONS: This study highlights the importance of monitoring for GBS symptoms following the administration of certain medications, particularly those that affect immune function, and underscores the need for healthcare providers to be aware of the potential neurological risks associated with these medications.

PMID:40220275 | DOI:10.1080/14740338.2025.2493781

Neurol Ther. 2025 Apr 12. doi: 10.1007/s40120-025-00739-5. Online ahead of print.

ABSTRACT

INTRODUCTION: All large studies evaluating the role of cilostazol versus other antiplatelet agents in stroke prevention have been conducted in Asia and included patients with minor stroke or transient ischemic attack (TIA). Ours is the first-ever trial to evaluate the safety and efficacy of cilostazol versus clopidogrel in moderate and moderate-to-severe ischemic stroke in North Africa. Accordingly, in this study we assess the role of cilostazol as an alternative to clopidogrel in Egyptian patients with first-ever non-cardioembolic moderate or moderate-to-severe ischemic stroke.

METHODS: A total of 870 patients with moderate and moderate-to-severe acute ischemic stroke (AIS) were randomly assigned to administration of loading and maintenance doses of cilostazol or clopidogrel.

RESULTS: Of the 870 patients included in our trial, 37 (8.7%) in the cilostazol arm and 59 (13.6%) in the clopidogrel arm experienced a new stroke (HR 0.53; 95% CI, 0.33-0.84; P = 0.007). Twelve participants (2.8%) in the cilostazol group and 25 patients (5.7%) in the clopidogrel group experienced drug-related hemorrhagic complications (HR 0.25; 95% CI, 0.12-0.53; P = 0.001). Patients with hypertension who received cilostazol had significantly lower rates of recurrent hemorrhagic and ischemic stroke.

CONCLUSION: Egyptian patients with non-cardioembolic moderate and moderate-to-severe ischemic stroke who received cilostazol within the first 24 h of symptoms had significantly lower rates of hemorrhagic transformation of brain infarction and peripheral hemorrhagic complications than those who received clopidogrel. Patients with hypertension achieved the greatest benefit from cilostazol, as they experienced a significant reduction in recurrent ischemic and hemorrhagic infarction. There were no significant differences between the two groups regarding the modified Rankin scale (mRS) score after 3 months or in the non-hemorrhagic side effects. Our results were derived from a single-blinded study; a more extensive, double-blinded, multinational study is needed for the results to be generalizable worldwide.

TRIAL REGISTRATION: Retrospectively registered, ClinicalTrials.gov, NCT06242132, 27-01-2024.

PMID:40220202 | DOI:10.1007/s40120-025-00739-5

J Appl Clin Med Phys. 2025 Apr 11:e70092. doi: 10.1002/acm2.70092. Online ahead of print.

ABSTRACT

PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).

METHODS: Postoperative PDAC patients were retrospectively selected, all of whom had undergone preoperative CT imaging and surgery. Both patients with resectable or borderline-resectable pancreatic cancer met the eligibility criteria in this study. However, owing to the differences in treatment strategies and such, this research mainly focused on patients with resectable pancreatic cancer. All patients were subject to follow-up assessments for a minimum of 9 months. A total of 250 cases meeting the inclusion criteria were included. A clinical model, a conventional radiomics model, and a deep-radiomics model were constructed for ER prediction (defined as occurring within 9 months) in the training set. A model based on the TNM staging was utilized as a baseline for comparison. Assessment of the models' performance was based on the area under the receiver operating characteristic curve (AUC). Additionally, precision-recall (PR) analysis and calibration assessments were conducted for model evaluation. Furthermore, the clinical utility of the models was evaluated through decision curve analysis (DCA), net reclassification improvement (NRI), and improvement of reclassification index (IRI).

RESULTS: In the test set, the AUC values for ER prediction were as follows: TNM staging, ROC-AUC = 0.673 (95% CI: 0.550, 0.795), PR-AUC = 0.362 (95% CI: 0.493, 0.710); clinical model, ROC-AUC = 0.640 (95% CI: 0.504, 0.775), PR-AUC = 0.481 (95% CI: 0.520, 0.735); radiomics model, ROC-AUC = 0.722 (95% CI: 0.604, 0.839), PR-AUC = 0.575 (95% CI: 0.466, 0.686); and deep-radiomics model, which exhibited the highest ROC-AUC of 0.895 (95% CI: 0.820, 0.970), PR-AUC = 0.834 (95% CI: 0.767, 0.923). The difference in both ROC-AUC and PR-AUC for the deep-radiomics model was statistically significant when compared to the other scores (all p < 0.05). The DCA curve of the deep-radiomics model outperformed the other models. NRI and IRI analyses demonstrated that the deep-radiomics model significantly enhances risk classification compared to the other prediction methods (all p < 0.05).

CONCLUSION: The predictive performance of deep features based on CT images exhibits favorable outcomes in predicting early recurrence.

PMID:40217563 | DOI:10.1002/acm2.70092

Sci Rep. 2025 Apr 11;15(1):12421. doi: 10.1038/s41598-025-95959-y.

ABSTRACT

In recent years, landslides have occurred frequently around the world, resulting in significant casualties and property damage. A notable example occurred in 2014, when a landslide in the Argo region of Afghanistan claimed over 2000 lives, becoming one of the most devastating landslide events in history. The increasing frequency and severity of landslides present significant challenges to geological disaster monitoring, making the development of efficient and accurate detection methods critical for disaster mitigation and prevention. This study proposes an intelligent recognition method for landslides, which is based on the latest deep learning model, YOLOv11-seg, which is designed to address the challenges posed by complex terrains and the diverse characteristics of landslides. Using the Bijie-Landslide dataset, the method optimizes the feature extraction and segmentation modules of YOLOv11-seg, enhancing both the accuracy of landslide boundary detection and the pixel-level segmentation of landslide areas. Compared with traditional methods, YOLOv11-seg performs better in detecting complex boundaries and handling occlusion, demonstrating superior detection accuracy and segmentation quality. During the preprocessing phase, various data augmentation techniques, including mirroring, rotation, and color adjustment, were employed, significantly improving the model's generalization performance and robustness across varying terrains, seasons, and lighting conditions. The experimental results indicate that the YOLOv11-seg model excels in several key performance metrics, such as precision, recall, F1 score, and mAP. Specifically, the F1 score reaches 0.8781 for boundary detection and 0.8114 for segmentation, whereas the mAP for bounding box (B) detection and mask (M) segmentation tasks outperforms traditional methods. These results highlight the high reliability and adaptability of YOLOv11-seg for landslide detection. This research provides new technological support for intelligent landslide monitoring and risk assessment, highlighting its potential in geological disaster monitoring.

PMID:40216897 | DOI:10.1038/s41598-025-95959-y

Sci Rep. 2025 Apr 11;15(1):12495. doi: 10.1038/s41598-025-91575-y.

ABSTRACT

Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)-based approach for predicting programmed death-ligand 1 (PD-L1) expression in patients with non-small cell lung cancer (NSCLC). Data from 352 NSCLC patients with known PD-L1 expression were collected, of which 48.29% (170/352) were tested positive for PD-L1 expression. Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. Seven algorithms were used to build models, and the most optimal ones were identified. A combined model integrating DLR with clinical data was also developed. The predictive performance of each model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve analysis. The DLR model, based on CT images, demonstrated an AUC of 0.85 (95% confidence interval (CI), 0.82-0.88), sensitivity of 0.80 (0.74-0.85), and specificity of 0.73 (0.70-0.77) for predicting PD-L1 status. The integrated model exhibited superior performance, with an AUC of 0.91 (0.87-0.95), sensitivity of 0.85 (0.82-0.89), and specificity of 0.75 (0.72-0.80). Our findings indicate that the DLR model holds promise as a valuable tool for predicting the PD-L1 status in patients with NSCLC, which can greatly assist in clinical decision-making and the selection of personalized treatment strategies.

PMID:40216830 | DOI:10.1038/s41598-025-91575-y

Sci Rep. 2025 Apr 11;15(1):12380. doi: 10.1038/s41598-025-95364-5.

ABSTRACT

Composites are widely used in wind turbine blades due to their excellent strength-to-weight ratio and operational flexibilities. However, wind turbines often operate in harsh environmental conditions that can lead to various types of damage, including abrasion, corrosion, fractures, cracks, and delamination. Early detection through structural health monitoring (SHM) is essential for maintaining the efficient and reliable operation of wind turbines, minimizing downtime and maintenance costs, and optimizing energy output. Further, Damage detection and localization are challenging in curved composites due to their anisotropic nature, edge reflections, and generation of higher harmonics. Previous work has focused on damage localization using deep-learning approaches. However, these models are computationally expensive, and multiple models need to be trained independently for various tasks such as damage classification, localization, and sizing identification. Also, the data generated due to AE waveforms at a minimum sampling rate of 1MSPS is huge, requiring tinyML enabled hardware for real time ML models which can reduce the size of cloud storage required. TinyML hardware can run ML models efficiently with low power consumption. This paper presents a Hybrid Hierarchical Machine-Learning Model (HHMLM) that leverages acoustic emission (AE) data to identify, classify, and locate different types of damage using the single unified model. The AE data is collected using a single sensor, with damage simulated by artificial AE sources (Pencil lead break) and low-velocity impacts. Additionally, simulated abrasion on the blade's leading edge resembles environmental wear. This HHMLM model achieved 96.4% overall accuracy with less computation time than 83.8% for separate conventional Convolutional Neural Network (CNN) models. The developed SHM solution provides a more effective and practical solution for in-service monitoring of wind turbine blades, particularly in wind farm settings, with the potential for future wireless sensors with tiny ML applications.

PMID:40216825 | DOI:10.1038/s41598-025-95364-5

NPJ Antimicrob Resist. 2025 Apr 11;3(1):25. doi: 10.1038/s44259-025-00097-0.

ABSTRACT

Slow- and non-growing bacterial populations, along with intracellular pathogens, often evade standard antibacterial treatments and are linked to persistent and recurrent infections. This necessitates the development of therapies specifically targeting nonproliferating bacteria. To identify compounds active against non-growing uropathogenic Escherichia coli (UPEC) we performed a drug-repurposing screen of 6454 approved drugs and drug candidates. Using dilution-regrowth assays, we identified 39 compounds that either kill non-growing UPEC or delay its regrowth post-treatment. The hits include fluoroquinolones, macrolides, rifamycins, biguanide disinfectants, a pleuromutilin, and anti-cancer agents. Twenty-nine of the hits have not previously been recognized as active against non-growing bacteria. The hits were further tested against non-growing Pseudomonas aeruginosa and Staphylococcus aureus. Ten compounds - solithromycin, rifabutin, mitomycin C, and seven fluoroquinolones-have strong bactericidal activity against non-growing P. aeruginosa, killing >4 log10 of bacteria at 2.5 µM. Solithromycin, valnemulin, evofosfamide, and satraplatin are unique in their ability to selectively target non-growing bacteria, exhibiting poor efficacy against growing bacteria. Finally, 31 hit compounds inhibit the growth of intracellular Shigella flexneri in a human enterocyte infection model, indicating their ability to permeate the cytoplasm of host cells. The identified compounds hold potential for treating persistent infections, warranting further comparative studies with current standard-of-care antibiotics.

PMID:40216902 | DOI:10.1038/s44259-025-00097-0

Exp Cell Res. 2025 Apr 9:114551. doi: 10.1016/j.yexcr.2025.114551. Online ahead of print.

ABSTRACT

Due to the expensive and lengthy process of drug design and approval, drug repurposing (or repositioning) has become another option for identifying preexisting molecules that may be used for alternative purposes. Recently, some antimalarial compounds have been shown to display efficacy against cancer cell proliferation. In this study, we provide evidence to suggest that multiple preexisting antimalarial drugs can reduce the viability of human cancer cells in culture. Furthermore, we provide the first evidence that one antimalarial, Tafenoquine (LD50=9.6μM in HCT116 cells), is capable of decreasing viability with an efficacy comparable to Etoposide (LD50=15.2μM in HCT116 cells) Further, Tafenoquine induces apoptosis and increases the expression of genes involved in cell cycle arrest and cell death. We also show that cells are sensitized to the apoptotic effects of Tafenoquine following depletion of the heme oxygenase 1 (HMOX-1) gene. Collectively, our studies confirm that antimalarial compounds hold the potential for use as anticancer agents and provide the first evidence to detail the potent efficacy of Tafenoquine against cancer cells in culture.

PMID:40216009 | DOI:10.1016/j.yexcr.2025.114551

Biomed Pharmacother. 2025 Apr 10;186:118031. doi: 10.1016/j.biopha.2025.118031. Online ahead of print.

ABSTRACT

The coagulation system plays a complex role in cancer therapy. Endothelial damage and tissue factor increased by chemotherapy initiate the coagulation cascade, producing active FXa and releasing thrombin. Thrombin triggers tumor growth and metastasis, leading to severe thromboembolic events in cancer patients. Direct thrombin inhibitors do not have the expected anti-metastatic effect as PAR-2 remains active and increases the risk of bleeding. Therefore, dual inhibition of thrombin by FXa inhibition and plasmin inhibition, which converts fibrin to fibrinogen, is targeted. Clinical studies show that the use of tranexamic acid in patients on NOAC therapy may be beneficial without increasing the risk of bleeding. This approach offers a promising strategy to provide an anti-metastatic effect in cancer treatment.

PMID:40215647 | DOI:10.1016/j.biopha.2025.118031

BMJ Paediatr Open. 2025 Apr 10;9(1):e003286. doi: 10.1136/bmjpo-2024-003286.

ABSTRACT

BACKGROUND: The National Health Service in the UK is the first national healthcare system to offer genomic sequencing for rare disease diagnosis as routine care. Non-genetic medical specialists, including paediatricians, can now request genomic testing for certain clinical indications. The primary purpose of this study was to evaluate the preparedness and confidence of paediatricians providing genomic sequencing in England. In addition, we assessed current practice, perceived utility of testing, barriers and enablers, prior genomics education and training preferences.

METHODS: A 26-item electronic survey for completion by paediatric specialists. Participants were recruited through national associations and a conference. Quantitative items were analysed using descriptive and inferential statistics. Open-ended question responses were analysed by qualitative content analysis.

RESULTS: 157 responses were included in the analysis. Only 49.0% reported feeling prepared for mainstreaming despite 75.0% reporting they had requested testing in the past 12 months, 47.7% indicating they had returned genomic sequencing results and 67.1% feeling genomic testing was useful. Mean confidence scores were lowest for tasks including using human phenotype ontology terminology on test request forms (3.9/10), interpreting genomic test results (4.8/10), discussing complex genomic results with patients and families (4.3/10) and integrating test results into patient care (4.7/10). Significantly higher average ranked genomic confidence was identified among those who had requested testing in the last 12 months compared with those who had not (Z=5.063, p<0.001, r=0.412). The most frequent barriers to mainstreaming were lack of training and knowledge (43.3%), determining patient eligibility (28.0%), lack of time (27.4%) and confidence (25.5%). Webinars (48.4%), followed by continued professional development meetings and/or conferences (38.9%), were the preferred mode of training.

CONCLUSIONS: Our data suggest that preparedness and confidence among paediatricians in genomics is currently lacking. Support from clinical genetics services, simplified referral forms and webinar training sessions could improve current practice.

PMID:40216446 | DOI:10.1136/bmjpo-2024-003286

Inflammopharmacology. 2025 Apr 12. doi: 10.1007/s10787-025-01725-x. Online ahead of print.

NO ABSTRACT

PMID:40216663 | DOI:10.1007/s10787-025-01725-x

OMICS. 2025 Apr 11. doi: 10.1089/omi.2024.0199. Online ahead of print.

ABSTRACT

Anophthalmia is the most severe ocular malformation inherited in an autosomal, X-linked, recessive, or dominant form. We report here the use of whole exome sequencing (WES) to help the clinical diagnosis of familial anophthalmia in Harare, Zimbabwe. A mother presented her two sons, who are half-brothers, at the Eye, Ear, Nose, and Throat Institute, Ophthalmology Unit in Harare, Zimbabwe. Upon clinical examination, half-brothers were diagnosed with clinical bilateral anophthalmia. The mother requested a genetic diagnosis for her two sons. To segregate the phenotype with genotype, whole blood was collected from two half-brothers, their mother, maternal aunt, and maternal uncle to the half-brothers, and an unrelated healthy control. Genetic characterization was done, first, through a candidate gene approach screening of putative genes SOX2, OTX2, VSX2, PAX6, and RAX. When no causative variants were identified, the next step employed WES. Variants in 80 genes associated with anophthalmia were prioritized and subjected to pathogenicity testing. One pathogenic variant, BCOR c.254C>T (rs121434618, p. Pro85Leu), segregated with the mother and her two sons. The present clinical genomics study of a family and a healthy control sample underscores WES as a valuable tool that can help clinical diagnosis of anophthalmia in the Zimbabwean clinical setting. In this article, we also offer a reasoned discussion and call from the field, to fund clinical genomics and omics research and development in planetary health, especially in the current era of uncertainties in international aid and funding of innovative technologies. The findings reported herein encourage further research on the clinical utility of WES as a diagnostic tool in Africa and around the world as well, given that the candidate gene approach might miss the important genes or variants of relevance to disease pathophysiology.

PMID:40216558 | DOI:10.1089/omi.2024.0199

BMJ Health Care Inform. 2025 Apr 10;32(1):e101163. doi: 10.1136/bmjhci-2024-101163.

ABSTRACT

STUDY OBJECTIVES: Describe the implementation of an interoperable solution to support pharmacogenomic-guided prescribing in primary care in the National Health Service, England.

METHODS: We used an iterative approach to software development going through clinical workflow mapping, architecture design and development, and pilot-testing.

RESULTS: We configured a commercial health data management platform to store pharmacogenomic results in a structured format and created a knowledge base of pharmacogenomic guidance. This solution was deployed 'as-a-service' using an open application programming interface (API) specification, allowing third parties to receive pharmacogenomic results and guidance by querying the service using a patient identifier and medicine code. This was integrated with existing clinical decision support tools and presented contextual information to prescribers within their native electronic health record (EHR).

DISCUSSION: Pharmacogenomic results and guidance will be used across care settings and have greatest utility at the point of prescribing. This requires a solution, which separates the data from the applications, allowing integration with different EHRs through APIs.

CONCLUSIONS: A vendor-agnostic standards-based solution can support the implementation of pharmacogenomic-guided prescribing across primary care.

PMID:40216452 | DOI:10.1136/bmjhci-2024-101163

Toxicol Appl Pharmacol. 2025 Apr 9:117341. doi: 10.1016/j.taap.2025.117341. Online ahead of print.

ABSTRACT

Amiodarone is an effective therapy for arrhythmias, its prolonged management may lead to significant adverse drug reactions. Amiodarone-induced hepatotoxicity is described by phospholipidosis, hepatic steatosis, cholestatic hepatitis, and cirrhosis. However, the systemic and hepatic lipidome disturbances and underlying toxicological mechanisms remain comprehensively elucidated. Untargeted lipidomics were utilized to analyze serum and liver samples from the rats orally administered a daily dose of amiodarone of either 100 or 300 mg/kg for one week. Changes in the expression of hepatic lipid-related genes were also examined utilizing transcriptomics. We found a higher magnitude of lipidome alterations in the 300 mg/kg than those in the 100 mg/kg groups. Treated animals showed elevated abundances of phosphatidylcholines, ether-linked phosphatidylcholines, sphingomyelins, and ceramides, and decreased levels of triacylglycerols, ether-linked triacylglycerols, and fatty acids. We also found 199 lipid-related differentially expressed hepatic genes between the 300 mg/kg group versus controls, implying lipid metabolism and signaling pathways disturbances. Specifically, elevation of serum phosphatidylcholines and ether-linked phosphatidylcholines, as well as hepatic bismonoacylglycerophosphates were associated with reduced expression of phospholipase genes and elevated expression of glycerophospholipid biosynthesis genes, possibly driving phospholipidosis. Perturbations of sphingolipid metabolism might also be the key events for amiodarone-induced toxicity. Alterations in gene expression levels related to lipid storage and metabolism, mitochondria functions, and energy homeostasis were also found. Collectively, our study characterized the sophisticated perturbations in the lipidome and transcriptome of amiodarone-treated rats and suggested potential mechanisms responsible for amiodarone-induced hepatotoxicity.

PMID:40216313 | DOI:10.1016/j.taap.2025.117341

Vet J. 2025 Apr 9:106347. doi: 10.1016/j.tvjl.2025.106347. Online ahead of print.

ABSTRACT

Multimodal analgesic administration is a promising strategy for mitigating side effects typically associated with analgesia; nevertheless, variation in analgesic effectiveness still poses a considerable safety concern for both horses and veterinarians. Pharmacogenomic studies have started delving into genetic influences on varying drug effectiveness and related side effects. However, current findings have narrow implications and are limited in their ability to individualize analgesic dosages in horses. Hydromorphone and detomidine were administered to a cohort of 48 horses at standardized time intervals, with dosage rates recorded. Analgesic effectiveness was scored (1-3) based on pain response to dura penetration during cerebrospinal fluid centesis. Genome-wide association (GWA) analyses identified two SNVs passing the nominal significance threshold (P<1×10-5) in association with analgesic effectiveness. One SNV identified on chromosome 27 (rs1142378599) is contained within the LOC100630731 disintegrin and metalloproteinase domain-containing protein 5 gene. The second identified SNV is an intergenic variant located on chromosome 29 (rs3430772468) These SNVs accounted for 26.11% and 31.72% of explained variation in analgesic effectiveness respectively, with all eight of the horses with the lowest analgesic effectiveness expressing the A/C genotype at rs3430772468, with six of which also expressing the C/T genotype at rs1142872965. Whilst highlighting the multifactorial nature of analgesic efficacy, this study serves as an important step in the application of genome-wide approaches to better understand genetic factors underpinning commonly observed variation in analgesic effectiveness in horses, with the goal of tailoring analgesic dosage to minimize commonly observed side effects and improve the outcomes of equine pain management.

PMID:40216012 | DOI:10.1016/j.tvjl.2025.106347

Biomed Pharmacother. 2025 Apr 10;186:118040. doi: 10.1016/j.biopha.2025.118040. Online ahead of print.

ABSTRACT

Human fibroblast-like synoviocytes (HFLS) are the predominant cellular component of the joint synovium. Their inflammation, known as synovitis, may contribute to the development of osteoarthritis (OA). HFLS secrete signaling factors that regulate joint function in response to mechanical trauma or OA progression. Among these factors, prostaglandin E2 (PGE2) is a key pro-inflammatory mediator, whereas prostamides, such as prostamide E2 (PME2), are synthesized from anandamide (AEA) by the same enzymes that produce PGE2. HFLS were isolated from both control subjects and OA patients (HFLS-OA) and stimulated with lipopolysaccharide (LPS, 10 ng/mL). Liquid chromatography-tandem mass spectrometry (LC-MS) was used to analyze PGE2 and PME2 secretion. Additionally, transcriptome and miRNA sequencing were conducted to identify changes in gene expression between HFLS and HFLS-OA cells. Five endocannabinoid-related genes were further validated by qPCR. Baseline PGE2 secretion differed between HFLS and HFLS-OA, with OA-related cells showing increased levels, while control cells primarily produced PME2. Upon pro-inflammatory stimulation, both cell types secreted PGE2. Changes in endocannabinoid levels and expression of endocannabinoid-related genes were observed in HFLS-OA following stimulation. miRNA sequencing revealed significant differences in miRNA expression between HFLS and HFLS-OA. Notably, HFLS-OA exhibited upregulation of Diacylglycerol lipase B (DAGLB) and downregulation of Fatty Acid-Binding Protein 4 and 5 (FABP4 and FABP5) gene expression compared to controls. The study suggests a reorganization of the endocannabinoid system in HFLS from OA patients, leading to altered cellular responses to pro-inflammatory stimuli. The molecular changes observed may drive or regulate the inflammatory response in OA synoviocytes, highlighting potential therapeutic targets. These findings provide insights into the potential mechanisms underlying OA pathogenesis and support the hypothesis of altered endocannabinoid system reactivity in HFLS in the context of inflammation.

PMID:40215649 | DOI:10.1016/j.biopha.2025.118040

Int J Retina Vitreous. 2025 Apr 11;11(1):44. doi: 10.1186/s40942-025-00669-2.

ABSTRACT

BACKGROUND: Artificial intelligence (AI)-driven biomarker segmentation offers an objective and reproducible approach for quantifying key anatomical features in neovascular age-related macular degeneration (nAMD) using optical coherence tomography (OCT). Currently, Faricimab, a novel bispecific inhibitor of vascular endothelial growth factor (VEGF) and angiopoietin-2 (Ang-2), offers new potential in the management of nAMD, particularly in treatment-resistant cases. This study utilizes an advanced deep learning-based segmentation algorithm to analyze OCT biomarkers and evaluate the efficacy and durability of Faricimab over nine months in patients with therapy-refractory nAMD.

METHODS: This retrospective real-world study analyzed patients with treatment-resistant nAMD who switched to Faricimab following inadequate responses to ranibizumab or aflibercept. Automated segmentation of key OCT biomarkers - including fibrovascular pigment epithelium detachment (fvPED), intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), choroidal volume, and central retinal thickness (CRT) - was conducted using a deep learning algorithm based on a convolutional neural network.

RESULTS: A total of 46 eyes from 41 patients completed the nine-month follow-up. Significant reductions in SRF, fvPED, and choroidal volume were observed from baseline (mo0) to three months (mo3) and sustained at nine months (mo9). CRT decreased significantly from 342.7 (interquartile range (iqr): 117.1) µm at mo0 to 296.6 (iqr: 84.3) µm at mo3 and 310.2 (iqr: 93.6) µm at mo9. The deep learning model provided precise quantification of biomarkers, enabling reliable tracking of disease progression. The median injection interval extended from 35 (iqr: 15) days at mo0 to 56 (iqr: 20) days at mo9, representing a 60% increase. Visual acuity remained stable throughout the study. Correlation analysis revealed that higher baseline CRT and fvPED volumes were associated with greater best-corrected visual acuity (BCVA) improvements and longer treatment intervals.

CONCLUSIONS: This study highlights the potential of AI-driven biomarker segmentation as a precise and scalable tool for monitoring disease progression in treatment-resistant nAMD. By enabling objective and reproducible analysis of OCT biomarkers, deep learning algorithms provide critical insights into treatment response. Faricimab demonstrated significant and sustained anatomical improvements, allowing for extended treatment intervals while maintaining disease stability. Future research should focus on refining AI models to improve predictive accuracy and assessing long-term outcomes to further optimize disease management.

TRIAL REGISTRATION: Ethics approval was obtained from the Institutional Review Board of LMU Munich (study ID: 20-0382). This study was conducted in accordance with the Declaration of Helsinki.

PMID:40217505 | DOI:10.1186/s40942-025-00669-2

Arthritis Res Ther. 2025 Apr 11;27(1):85. doi: 10.1186/s13075-025-03508-9.

ABSTRACT

BACKGROUND: The modified Rodnan skin score (mRSS), a measure of systemic sclerosis (SSc) skin thickness, is agnostic to inflammation and vasculopathy. Previously, we demonstrated the potential of neural network-based digital pathology applied to SSc skin biopsies as a quantitative outcome. Here, we leverage deep learning and histologic analyses of clinical trial biopsies to decipher SSc skin features 'seen' by artificial intelligence (AI).

METHODS: Adults with diffuse cutaneous SSc ≤ 6 years were enrolled in an open-label trial of belumosudil [a Rho-associated coiled-coil containing protein kinase 2 (ROCK2) inhibitor]. Participants underwent serial mRSS and arm biopsies at week (W) 0, 24 and 52. Two blinded dermatopathologists scored stained sections (e.g., Masson's trichrome, hematoxylin and eosin, CD3, α-smooth muscle actin) for 16 published SSc dermal pathological parameters. We applied our deep learning model to generate QIF signatures/biopsy and obtain 'Fibrosis Scores'. Associations between Fibrosis Score and mRSS (Spearman correlation), and between Fibrosis Score and mRSS versus histologic parameters [odds ratios (OR)], were determined.

RESULTS: Only ten patients were enrolled due to early study termination, and of those, five had available biopsies due to fixation issues. Median, interquartile range (IQR) for mRSS change (0-52 W) for the ten participants was -2 (-9-7.5) and for the five with biopsies was -2.5 (-11-7.5). The correlation between Fibrosis Score and mRSS was R = 0.3; p = 0.674. Per 1-unit mRSS change (0-52 W), histologic parameters with the greatest associated changes were (OR, 95% CI, p-value): telangiectasia (2.01, [(1.31-3.07], 0.001), perivascular CD3 + (0.99, [0.97-1.02], 0.015), and % of CD8 + among CD3 + (0.95, [0.89-1.01], 0.031). Likewise, per 1-unit Fibrosis Score change, parameters with greatest changes were (OR, p-value): hyalinized collagen (1.1, [1.04 - 1.16], < 0.001), subcutaneous (SC) fat loss (1.47, [1.19-1.81], < 0.001), thickened intima (1.21, [1.06-1.38], 0.005), and eccrine entrapment (1.14, [1-1.31], 0.046).

CONCLUSIONS: Belumosudil was associated with non-clinically meaningful mRSS improvement. The histologic features that significantly correlated with Fibrosis Score changes (e.g., hyalinized collagen, SC fat loss) were distinct from those associated with mRSS changes (e.g., telangiectasia and perivascular CD3 +). These data suggest that AI applied to SSc biopsies may be useful for quantifying pathologic features of SSc beyond skin thickness.

PMID:40217251 | DOI:10.1186/s13075-025-03508-9

BMC Med Inform Decis Mak. 2025 Apr 11;25(1):161. doi: 10.1186/s12911-025-02989-7.

ABSTRACT

BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultrasound-guided thermal ablation can shrink nodules and alleviate discomfort. Because the degree and rate of lesion absorption vary greatly between individuals, there is no reliable model for predicting the therapeutic efficacy of thermal ablation.

METHODS: Five convolutional neural network models including VGG19, Resnet 50, EfficientNetB1, EfficientNetB0, and InceptionV3, pre-trained with ImageNet, were compared for predicting the efficacy of ultrasound-guided microwave ablation (MWA) for benign thyroid nodules using ultrasound data. The patients were randomly assigned to one of two data sets: training (70%) or validation (30%). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were all used to assess predictive performance.

RESULTS: In the validation set, fine-tuned EfficientNetB1 performed best, with an AUC of 0.85 and an ACC of 0.79.

CONCLUSIONS: The study found that our deep learning model accurately predicts nodules with VRR < 50% after a single MWA session. Indeed, when thermal therapies compete with surgery, anticipating which nodules will be poor responders provides useful information that may assist physicians and patients determine whether thermal ablation or surgery is the preferable option. This was a preliminary study of deep learning, with a gap in actual clinical applications. As a result, more in-depth study should be undertaken to develop deep-learning models that can better help clinics. Prospective studies are expected to generate high-quality evidence and improve clinical performance in subsequent research.

PMID:40217199 | DOI:10.1186/s12911-025-02989-7