Clin Implant Dent Relat Res. 2025 Feb;27(1):e70000. doi: 10.1111/cid.70000.

ABSTRACT

OBJECTIVES: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.

MATERIALS AND METHODS: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized. Two deep-learning models were trained using the YOLOv8 algorithm. The first model classified the regions of the jaw to number the teeth and identify implant regions, while the second model performed implant segmentation. Performance metrics including precision, recall, and F1-score were used to evaluate the model's effectiveness.

RESULTS: The implant segmentation model achieved a precision of 91.4%, recall of 90.5%, and an F1-score of 93.1%. For the implant-numbering task, precision ranged from 0.94 to 0.981, recall from 0.895 to 0.956, and F1-scores from 0.917 to 0.966 across various jaw regions. The analysis revealed that implants were most frequently located in the maxillary posterior region.

CONCLUSIONS: The AI model demonstrated high accuracy in detecting and numbering dental implants in panoramic radiographs. This technology offers the potential to reduce clinicians' workload and improve diagnostic accuracy in dental implantology. Further validation across more diverse datasets is recommended to enhance its clinical applicability.

CLINICAL RELEVANCE: This AI model could revolutionize dental implant detection and classification, providing fast, objective analyses to support clinical decision-making in dental practices.

PMID:39846131 | DOI:10.1111/cid.70000

Bioinform Adv. 2024 Nov 25;5(1):vbae187. doi: 10.1093/bioadv/vbae187. eCollection 2025.

ABSTRACT

SUMMARY: Protein structure prediction aims to infer a protein's three-dimensional (3D) structure from its amino acid sequence. Protein structure is pivotal for elucidating protein functions, interactions, and driving biotechnological innovation. The deep learning model AlphaFold2, has revolutionized this field by leveraging phylogenetic information from multiple sequence alignments (MSAs) to achieve remarkable accuracy in protein structure prediction. However, a key question remains: how well does AlphaFold2 understand protein structures? This study investigates AlphaFold2's capabilities when relying primarily on high-quality template structures, without the additional information provided by MSAs. By designing experiments that probe local and global structural understanding, we aimed to dissect its dependence on specific features and its ability to handle missing information. Our findings revealed AlphaFold2's reliance on sterically valid C β for correctly interpreting structural templates. Additionally, we observed its remarkable ability to recover 3D structures from certain perturbations and the negligible impact of the previous structure in recycling. Collectively, these results support the hypothesis that AlphaFold2 has learned an accurate biophysical energy function. However, this function seems most effective for local interactions. Our work advances understanding of how deep learning models predict protein structures and provides guidance for researchers aiming to overcome limitations in these models.

AVAILABILITY AND IMPLEMENTATION: Data and implementation are available at https://github.com/ibmm-unibe-ch/template-analysis.

PMID:39846081 | PMC:PMC11751578 | DOI:10.1093/bioadv/vbae187

Eur Heart J Digit Health. 2024 Sep 24;6(1):137-146. doi: 10.1093/ehjdh/ztae072. eCollection 2025 Jan.

ABSTRACT

AIMS: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners. We assess the system's usability among novice users and evaluate its performance against expert interpretations and advanced deep learning (DL) tools.

METHODS AND RESULTS: We developed an implicit neural representation network to reconstruct a 3D cardiac volume from sequential multi-view 2DE images, followed by automatic segmentation of LV and RV areas to calculate volume sizes and EF values. Our study involved 127 patients to assess EF estimation accuracy against expert readings and two-dimensional (2D) video-based DL models. A subset of 56 patients was utilized to evaluate image quality and 3D accuracy and another 50 to test usability by novice users and across various ultrasound machines. CardiacField generated a 3D heart from 2D echocardiograms with <2 min processing time. The LVEF predicted by our method had a mean absolute error (MAE) of 2.48 % , while the RVEF had an MAE of 2.65 % .

CONCLUSION: Employing a straightforward apical ring scan with a cost-effective 2DE probe, our method achieves a level of EF accuracy for assessing LV and RV function that is comparable to that of three-dimensional echocardiography probes.

PMID:39846074 | PMC:PMC11750196 | DOI:10.1093/ehjdh/ztae072

Eur Heart J Digit Health. 2024 Oct 27;6(1):7-22. doi: 10.1093/ehjdh/ztae080. eCollection 2025 Jan.

ABSTRACT

Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as area under the curve (AUC) and heterogeneity across models. ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812-0.917) and 0.847 (95% CI: 0.766-0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734-0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice.

PMID:39846062 | PMC:PMC11750195 | DOI:10.1093/ehjdh/ztae080

Health Inf Sci Syst. 2025 Jan 17;13(1):17. doi: 10.1007/s13755-024-00333-3. eCollection 2025 Dec.

ABSTRACT

PURPOSE: This paper aims to develop a three-dimensional (3D) Alzheimer's disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.

METHODS: Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed. Firstly, a multi-scale decoupling block is designed to enhance the network's ability to extract fine-grained features by segregating convolutional channels. Subsequently, a self-attention block is constructed to extract and adaptively fuse features from three directions (sagittal, coronal and axial), so that more attention is geared towards brain lesion areas. Finally, a clustering loss function is introduced and combined with the cross-entropy loss to form a joint loss function for enhancing the network's ability to discriminate between different sample types.

RESULTS: The accuracy of our model is 0.985 for the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and 0.963 for the Australian Imaging, Biomarker & Lifestyle (AIBL) dataset, both of which are higher than the classification accuracy of similar tasks in this category. This demonstrates that our model can accurately distinguish between normal control (NC) and Alzheimer's Disease (AD), as well as between stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI).

CONCLUSION: The proposed AD prediction network exhibits competitive performance when compared with state-of-the-art methods. The proposed model successfully addresses the challenges of dealing with 3D sMRI image data and the limitations stemming from inadequate information in 2D sections, advancing the utility of predictive methods for AD diagnosis and treatment.

PMID:39846055 | PMC:PMC11748674 | DOI:10.1007/s13755-024-00333-3

Health Sci Rep. 2025 Jan 22;8(1):e70372. doi: 10.1002/hsr2.70372. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND AIM: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy. This review aims to assess the effectiveness of AI in pediatric epilepsy detection while considering the ethical implications surrounding its implementation.

METHODOLOGY: A comprehensive systematic review was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, and Medline. Search terms encompassed "pediatric epilepsy," "artificial intelligence," "machine learning," "ethical considerations," and "data security." Publications from the past decade were scrutinized for methodological rigor, with a focus on studies evaluating AI's efficacy in pediatric epilepsy detection and management.

RESULTS: AI systems have demonstrated strong potential in diagnosing and monitoring pediatric epilepsy, often matching clinical accuracy. For example, AI-driven decision support achieved 93.4% accuracy in diagnosis, closely aligning with expert assessments. Specific methods, like EEG-based AI for detecting interictal discharges, showed high specificity (93.33%-96.67%) and sensitivity (76.67%-93.33%), while neuroimaging approaches using rs-fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN-LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. Non-EEG sensor-based methods effectively identified nocturnal seizures, offering promising support for pediatric care. However, ethical considerations around privacy, data security, and model bias remain crucial for responsible AI integration.

CONCLUSION: While AI holds immense potential to enhance pediatric epilepsy management, ethical considerations surrounding transparency, fairness, and data security must be rigorously addressed. Collaborative efforts among stakeholders are imperative to navigate these ethical challenges effectively, ensuring responsible AI integration and optimizing patient outcomes in pediatric epilepsy care.

PMID:39846037 | PMC:PMC11751886 | DOI:10.1002/hsr2.70372

MethodsX. 2024 Dec 21;14:103131. doi: 10.1016/j.mex.2024.103131. eCollection 2025 Jun.

ABSTRACT

Recent advancements in artificial intelligence (AI) have increased interest in intelligent transportation systems, particularly autonomous vehicles. Safe navigation in traffic-heavy environments requires accurate road scene segmentation, yet traditional computer vision methods struggle with complex scenarios. This study emphasizes the role of deep learning in improving semantic segmentation using datasets like the Indian Driving Dataset (IDD), which presents unique challenges in chaotic road conditions. We propose a modified CANet that incorporates U-Net and LinkNet elements, focusing on accuracy, efficiency, and resilience. The CANet features an encoder-decoder architecture and a Multiscale Context Module (MCM) with three parallel branches to capture contextual information at multiple scales. Our experiments show that the proposed model achieves a mean Intersection over Union (mIoU) value of 0.7053, surpassing state-of-the-art models in efficiency and performance. Here we demonstrate:•Traditional computer vision methods struggle with complex driving scenarios, but deep learning based semantic segmentation methods show promising results.•Modified CANet, incorporating U-Net and LinkNet elements is proposed for semantic segmentation of unstructured driving scenarios.•The CANet structure consists of an encoder-decoder architecture and a Multiscale Context Module (MCM) with three parallel branches to capture contextual information at multiple scales.

PMID:39846010 | PMC:PMC11751566 | DOI:10.1016/j.mex.2024.103131

Eur J Obstet Gynecol Reprod Biol X. 2024 Dec 24;25:100362. doi: 10.1016/j.eurox.2024.100362. eCollection 2025 Mar.

ABSTRACT

This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores. Furthermore, it underscores the importance of integrating quantitative imaging features with clinical data to enhance diagnostic accuracy and optimize surgical planning. The potential of ML to predict surgical morbidity by analyzing demographic and obstetric factors is also explored. Emphasizing the need for standardized methodologies to ensure consistent feature extraction and model performance, this review advocates for the integration of radiomics and ML into clinical workflows, aiming to improve patient outcomes and foster a multidisciplinary approach in high-risk pregnancies. Future research should focus on larger datasets and validation of biomarkers to refine predictive models in obstetric care.

PMID:39845985 | PMC:PMC11751428 | DOI:10.1016/j.eurox.2024.100362

Genome Integr. 2025 Jan 22;15:e20240002. doi: 10.14293/genint.15.1.002. eCollection 2024.

ABSTRACT

Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability. This commentary aims to discuss two potential use cases for AI in the field of medicine and the overarching challenges involved in their implementation.

PMID:39845982 | PMC:PMC11752870 | DOI:10.14293/genint.15.1.002

Front Immunol. 2025 Jan 8;15:1436491. doi: 10.3389/fimmu.2024.1436491. eCollection 2024.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease, characterized by impaired wound repair, tissue remodeling and fibrosis. Immune system may participate in the development and progression of the disease as indicated by altered activity in IPF sufferers. This study investigates the immune response to the BNT162b2 COVID-19 vaccine in patients with IPF compared to healthy controls, with a particular focus on evaluation of antibody responses, interferon-gamma release, cytokine profiling and a broad panel of immune cell subpopulations. IPF patients without prior exposure to SARS-CoV-2 had undetectable levels of anti-N IgG antibodies, highlighting their lack of previous infection. After vaccination, IPF patients showed a significant increase in anti-S1 IgG and IgA antibodies, though their levels were lower compared to healthy controls and convalescent IPF patients. Additionally, IPF patients exhibited altered proportions of regulatory T cells (Tregs) and effector T lymphocytes (Teffs) before and after vaccination. Specifically, IPF patients had higher percentages of Tregs with a Th2 phenotype and Th17 Tregs, along with reduced proportions of Th1/17 Tregs. Teffs in IPF patients showed a decrease in Th1-like and Th2-like populations after vaccination. Moreover, IPF patients demonstrated elevated populations of cytotoxic T lymphocytes (Tc) before vaccination and increased levels of γδ Tc cells throughout the study. Alterations in cytokine profiles were also observed, IPF patients showed higher levels of IL-6 and IL-22 compared to healthy controls. These findings suggest a distinct immune response in IPF patients to the COVID-19 vaccine, characterized by differences in antibody production, T cell differentiation and cytokine secretion compared to healthy individuals.

PMID:39845961 | PMC:PMC11750670 | DOI:10.3389/fimmu.2024.1436491

Respir Res. 2025 Jan 22;26(1):34. doi: 10.1186/s12931-025-03107-x.

ABSTRACT

Progressive forms of interstitial lung diseases, including idiopathic pulmonary fibrosis (IPF), are deadly disorders lacking non-invasive biomarkers for assessment of early disease activity, which presents a major obstacle in disease management. Excessive extracellular matrix (ECM) deposition is a hallmark of these disorders, with fibronectin being an abundant ECM glycoprotein that is highly upregulated in early fibrosis and serves as a scaffold for the deposition of other matrix proteins. Due to its role in active fibrosis, we are targeting fibronectin as a biomarker of early lung fibrosis disease activity via the PEGylated fibronectin-binding polypeptide (PEG-FUD). In this work, we demonstrate the binding of PEG-FUD to the fibrotic lung throughout the course of bleomycin-induced murine model of pulmonary fibrosis. We first analyzed the binding of radiolabeled PEG-FUD following direct incubation to precision cut lung slices from mice at different stages of experimental lung fibrosis. Then, we administered fluorescently labeled PEG-FUD subcutaneously to mice over the course of bleomycin-induced pulmonary fibrosis and assessed peptide uptake 24 h later through ex vivo tissue imaging. Using both methods, we found that peptide targeting to the fibrotic lung is increased during the fibrogenic phase of the single dose bleomycin lung fibrosis model (days 7 and 14 post-bleomycin). At these timepoints we found a correlative relationship between peptide uptake and fibrotic burden. These data suggest that PEG-FUD targets fibronectin associated with active fibrogenesis in this model, making it a promising candidate for a clinically translatable molecular imaging probe to non-invasively determine pulmonary fibrosis disease activity, enabling accelerated therapeutic decision-making.

PMID:39844185 | DOI:10.1186/s12931-025-03107-x

Circ Genom Precis Med. 2025 Jan 23:e004594. doi: 10.1161/CIRCGEN.124.004594. Online ahead of print.

ABSTRACT

BACKGROUND: Transcriptional dysregulation, possibly affected by genetic variation, contributes to disease development. Due to dissimilarities in development, function, and remodeling during disease progression, transcriptional differences between the left atrial (LA) and right atrial (RA) may provide insight into diseases such as atrial fibrillation.

METHODS: Lateral differences in atrial transcription were evaluated in CATCH ME (Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly) using a 2-stage discovery and replication design. The design took advantage of the availability of 32 paired samples, for which both LA and RA tissue were obtained, as a discovery cohort, and 98 LA and 69 RA unpaired samples utilized as a replication cohort.

RESULTS: A total of 714 transcripts were identified and replicated as differentially expressed (DE) between LA and RA, as well as 98 exons in 55 genes. Approximately 50% of DE transcripts were colocated with another frequently correlated DE transcript (PFDR ≤0.05 for 579 regions). These transcription disequilibrium blocks contained examples including side-specific differential exon usage, such as the PITX2 locus, where ENPEP showed evidence of differential exon usage. Analysis of this region in conjunction with BMP10 identified rs9790621 as associated with ENPEP transcription in LA, while rs7687878 was associated with BMP10 expression in RA. In RA, BMP10 and ENPEP were strongly correlated in noncarriers, which was attenuated in risk-allele carriers, where BMP10 and PITX2 expression were strongly correlated.

CONCLUSIONS: These results significantly expand knowledge of the intricate, tissue-specific transcriptional landscape in human atria, including DE transcripts and side-specific isoform expression. Furthermore, they suggest the existence of blocks of transcription disequilibrium influenced by genetics.

PMID:39846178 | DOI:10.1161/CIRCGEN.124.004594

Front Pharmacol. 2025 Jan 8;15:1462193. doi: 10.3389/fphar.2024.1462193. eCollection 2024.

ABSTRACT

INTRODUCTION: Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability. Drug-specific models are often tied to commercial software and developed on various platforms and languages, potentially hindering access and complicating the comparison of different compounds.

METHODS: This work introduces stormTB: SimulaTOr of a muRine Minimal-pbpk model for anti-TB drugs. It is a web-based interface for our minimal physiologically based pharmacokinetic (mPBPK) platform, designed to simulate custom treatment scenarios for tuberculosis in murine models. The app facilitates visual comparisons of pharmacokinetic profiles, aiding in assessing drug-dose combinations.

RESULTS: The mPBPK model, supporting 11 anti-TB drugs, offers a unified perspective, overcoming the potential inconsistencies arising from diverse modeling efforts. The app, publicly accessible, provides a user-friendly environment for researchers to conduct what-if analyses and contribute to collective TB eradication efforts. The tool generates comprehensive visualizations of drug concentration profiles and pharmacokinetic/pharmacodynamic indices for TB-relevant tissues, empowering researchers in the quest for more effective TB treatments. stormTB is freely available at the link: https://apps.cosbi.eu/stormTB.

PMID:39845781 | PMC:PMC11750688 | DOI:10.3389/fphar.2024.1462193

Front Plant Sci. 2025 Jan 8;15:1446383. doi: 10.3389/fpls.2024.1446383. eCollection 2024.

ABSTRACT

INTRODUCTION: Rice samples exposed to the space environment have generated diverse phenotypic variations. Miniature-inverted-repeat transposable elements (MITEs), often found adjacent to genes, play a significant role in regulating the plant genome. Herein, the contribution of MITEs in regulating space-mutagenic phenotypes was explored.

METHODS: The space-mutagenic phenotype changes in the F3 to F5 generations of three space-mutagenic lines from the rice varieties Dongnong423 (DN423) and Dongnong (DN416) were meticulously traced. Rice leaves samples at the heading stage from three space-mutagenic lines were subjected to high coverage whole-genome bisulfite sequencing and whole-genome sequencing. These analyses were conducted to investigate the effects of MITEs related epigenetic and genetic variations on space-mutagenic phenotypes.

RESULTS AND DISCUSSION: Studies have indicated that MITEs within gene regulatory regions might contribute to the formation and differentiation of space-mutagenic phenotypes. The space environment has been shown to induce the transposable elements insertion polymorphisms of MITEs (MITEs-TIPs), with a notable preference for insertion near genes involved in stress response and phenotype regulation. The space-induced MITEs-TIPs contributed to the formation of space-mutagenic phenotype by modulating the expression of gene near the insertion site. This study underscored the pivotal role of MITEs in modulating plant phenotypic variation induced by the space environment, as well as the transgenerational stability of these phenotypic variants.

PMID:39845491 | PMC:PMC11751223 | DOI:10.3389/fpls.2024.1446383

Front Plant Sci. 2025 Jan 8;15:1457713. doi: 10.3389/fpls.2024.1457713. eCollection 2024.

NO ABSTRACT

PMID:39845489 | PMC:PMC11750989 | DOI:10.3389/fpls.2024.1457713

Microb Biotechnol. 2025 Jan;18(1):e70068. doi: 10.1111/1751-7915.70068.

NO ABSTRACT

PMID:39844583 | DOI:10.1111/1751-7915.70068

BMC Bioinformatics. 2025 Jan 22;26(1):26. doi: 10.1186/s12859-024-06026-8.

ABSTRACT

BACKGROUND: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.

RESULTS: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI). By combining the training of a structured collaborative-filtering classifier and an embedding learning task, JELI predicts new user-item associations based on jointly learned item and user embeddings while providing feature-wise importance scores. Therefore, JELI flexibly allows the introduction of priors on the connections between users, items, and features. In particular, JELI simultaneously (a) learns feature, item, and user embeddings; (b) predicts new item-user associations; (c) provides importance scores for each feature. Moreover, JELI instantiates a generic approach to training recommender systems by encoding generic graph-regularization constraints.

CONCLUSIONS: First, we show that the joint training approach yields a gain in the predictive power of the downstream classifier. Second, JELI can recover feature-association dependencies. Finally, JELI induces a restriction in the number of parameters compared to baselines in synthetic and drug-repurposing data sets.

PMID:39844056 | DOI:10.1186/s12859-024-06026-8

Acta Endocrinol (Buchar). 2024 Apr-Jun;20(2):249-255. doi: 10.4183/aeb.2024.249. Epub 2025 Jan 18.

ABSTRACT

INTRODUCTION: Diabetes mellitus, a chronic metabolic disorder stemming from pancreatic dysfunction, is surging in India, notably among those aged 60 and above. The escalating disease prevalence in this demographic necessitates heightened medication use, escalating the risk of Adverse Drug Reactions (ADRs). This underscores the vital role of ADR monitoring to curtail potential harm.

METHOD: A 12-month cross-sectional, prospective, observational study engaged 200 participants from the geriatric Outpatient Department (OPD). Diabetic patients in the geriatric OPD, willing to participate, underwent face-to-face evaluations using a structured questionnaire focused on adverse reactions to anti-diabetic medications. The study also included a Knowledge, Attitude, and Practice (KAP) assessment.

RESULTS: Of the 200 patients, 57% were male, 43% female. Thirteen participants (7 male, 6 female) reported ADR encounters during therapy, predominantly categorized as mild in causality and severity. KAP assessments unveiled a robust understanding of ADRs, primarily shaped by physicians and reinforced by pharmacists. Anticipation of ADR occurrence was noted in 70% of respondents, linked to non-compliance and lifestyle factors.

CONCLUSION: Educating caregivers about the critical importance of monitoring medication adherence among the elderly is imperative. Cultivating an attitude of reporting even minor ADRs to appropriate authorities is essential for harm prevention.

PMID:39845748 | PMC:PMC11750232 | DOI:10.4183/aeb.2024.249

Clin Transl Sci. 2025 Jan;18(1):e70134. doi: 10.1111/cts.70134.

ABSTRACT

The Timor-Leste Pharmacovigilance (PV) became an associate member of the WHO Programme for International Drug Monitoring in 2019; however, the adverse drug reaction (ADR) reporting rate remains low, with only nine reports per 1342 million inhabitants over 5 years. This study aimed to evaluate the knowledge, attitude, practice, and barriers related to ADRs, pharmacovigilance, and ADR reporting among healthcare professionals (HCPs) in Timor-Leste. A cross-sectional survey with a validated, self-administered questionnaire was conducted among 600 HCPs, including clinical doctors, nurses, and pharmacy employees from one national referral and five referral hospitals. Of the 461 HCPs who responded (76.8% response rate), 98 were clinical doctors (21.3%), 311 nurses (67.4%), and 52 pharmacy employees (11.3%). The knowledge score on ADRs was 3.81 ± 0.36 out of 8, with clinical doctors, nurses, and pharmacy employees scoring 4.49 ± 0.51, 3.47 ± 0.24, and 4.56 ± 0.26, respectively. On pharmacovigilance and ADR reporting, the score was 3.00 ± 0.16 out of 8, with clinical doctors, nurses, and pharmacy employees scoring 3.36 ± 0.26, 2.81 ± 0.08, and 3.50 ± 0.24, respectively. All scores referred to the number of correctly answered questions. Positive attitudes were observed, with 53.4% agreeing that ADR reporting is crucial for drug safety, although only 22.0% reported observed ADRs. Key barriers included unavailability of reporting forms (81.0%), insufficient financial support (71.9%), and lack of reporting by colleagues (71.4%). These findings highlight the need for increased awareness, training, and resources to improve ADR reporting in Timor-Leste.

PMID:39844473 | DOI:10.1111/cts.70134

BMC Immunol. 2025 Jan 23;26(1):4. doi: 10.1186/s12865-025-00682-y.

ABSTRACT

Vanishing bile duct syndrome (VBDS) is a serious drug induced liver injury characterized by chronic cholestasis and loss of intrahepatic bile ducts. VBDS has been reported also following checkpoint inhibitor treatment. We compared CD3 + , CD4 + , CD8 + , CD20 + , CD57 + , PD-1 + and PD-L1 + lymphocyte infiltrates in liver biopsies of patients that encountered VBDS (n = 2) or hepatotoxicity (n = 3) after pembrolizumab (n = 4) or nivolumab (n = 1) treatment with samples from normal liver (n = 10), non-alcohol steatohepatitis (NASH, n = 10), primary biliary cholangitis (PBC, n = 10) or pembrolizumab-treated patients without adverse events (n = 2). Notably, none of the cancer patients had primary nor metastatic liver tumors. We also studied direct growth effects of pembrolizumab on primary human intrahepatic biliary epithelial cells (HIBEpiC) in vitro. Liver sections of all checkpoint inhibitor- treated patients exhibited significantly higher CD3 + infiltration than normal livers, and significantly higher PD-L1 + , CD4 + and CD8 + infiltration, than other groups. PD-1 + infiltration was significantly increased in livers of patients with severe hepatic adverse event. CD57 + infiltration was similar in normal livers, NASH- and PBC groups, but highly increased in the checkpoint inhibitor-treated patients. Immune cell infiltrates were similar between NASH and normal livers. PBC samples had significantly higher CD3 + , CD4 + , CD8 + and CD20 + infiltrates than normal livers. HIBEpiC express PD-L1 but pembrolizumab did not affect their viability in vitro. Our findings suggest that VBDS is not due to direct cytotoxicity of checkpoint inhibitors and that the immunological attack against livers induced by these drugs is different from other cholestatic liver conditions.Biological insight: Checkpoint inhibitors upregulate PD-1 and PD-L1, as well as cytotoxic CD57 + cells in the non-cancerous liver tissues and this may be associated with checkpoint inhibitor-induced hepatotoxicity.

PMID:39844069 | DOI:10.1186/s12865-025-00682-y