Transplant Proc. 2025 Feb 24:S0041-1345(25)00083-1. doi: 10.1016/j.transproceed.2025.02.008. Online ahead of print.

ABSTRACT

Cytomegalovirus (CMV) is one of the most common opportunistic infections affecting solid organ transplant recipients (SOTRs). In this article, we presented the case of a 39-year-old patient with end-stage renal disease after kidney transplantation with refractory CMV infection, who was successfully treated with maribavir for the first time in Poland. The use of maribavir resulted in a significant reduction of CMV viremia in ganciclovir/valganciclovir-resistant CMV infection and resolution of CMV disease symptoms in the absence of drug-related adverse events.

PMID:40000309 | DOI:10.1016/j.transproceed.2025.02.008

Pharmacogenomics. 2025 Feb 25:1-18. doi: 10.1080/14622416.2025.2470605. Online ahead of print.

ABSTRACT

AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of lithium response in the treatment of bipolar disorder. We focused our research article on reports describing findings from genome-wide association studies, polygenic risk scores, and gene expression analyses associated with lithium response.

METHODS: We conducted a non-systematic review of studies from PubMed/Medline by using terms such as "pharmacogenomics," "GWAS," "gene expression," and "lithium response." Inclusion criteria focused on original research involving human subjects and assessing lithium response outcomes as well as in vitro studies. An extensive pearl-growing strategy was employed to further enlarge the scope of the piece by capitalizing on the knowledge of study authors on the subject.

RESULTS: The observed results, albeit promising, remain preliminary in terms of clinical relevance. Machine learning combining genetic and clinical data appears associated with moderate success in predicting lithium responsiveness. Gene expression studies and genome-wide association studies have helped identify possible targets of lithium and have the potential to support target-specific drug research.

CONCLUSIONS: Pharmacogenomics may support further discoveries in precision medicine further enlarging our understanding of the underlying mechanisms of lithium for its efficacy. However, clinical applications currently appear out of reach in the near future.

PMID:39998910 | DOI:10.1080/14622416.2025.2470605

Phys Med Biol. 2025 Feb 25. doi: 10.1088/1361-6560/adba39. Online ahead of print.

ABSTRACT

Objective:To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients.

Approach:A probabilistic daily anatomy model for head and neck patients (DAMHN) is built on the variational autoencoder architecture. The model approximates the generative joint conditional probability distribution of the repeat computed tomography (rCT) images and their corresponding masks on the planning CT images (pCT) and their masks. The model outputs deformation vector fields, which are used to produce possible rCTs and associated masks. The dataset is composed of 93 patients (i.e., 315 pCT - rCT pairs), 9 (i.e., 27 pairs) of which were set aside for final testing. The performance of the model is assessed based on the reconstruction accuracy and the generative performance for the set aside patients. 

Main results:The model achieves a DICE score of 0.83 and an image similarity score (NCC) of 0.60 on the test set. The generated parotid glands, spinal cord and constrictor muscle volume change distributions and center of mass shift distributions were also assessed. For all organs, the medians of the distributions are close to the true ones, and the distributions are broad enough to encompass the real observed changes. Moreover, the generated images display anatomical changes in line with the literature reported ones, such as the medial shifts of the parotids glands. 

Significance:DAMHNis capable of generating realistic anatomies observed during the course of the treatment and has applications in anatomical robust optimization, treatment planning based on plan library approaches and robustness evaluation against inter-fractional changes.

PMID:39999567 | DOI:10.1088/1361-6560/adba39

Comput Biol Med. 2025 Feb 24;188:109857. doi: 10.1016/j.compbiomed.2025.109857. Online ahead of print.

ABSTRACT

Explainability in Medical Computer Vision is one of the most sensible implementations of Artificial Intelligence nowadays in healthcare. In this work, we propose a novel Deep Learning architecture for eXplainable Artificial Intelligence, specially designed for medical diagnostic. The proposed approach leverages Variational Autoencoders properties to produce linear modifications of images in a lower-dimensional embedded space, and then reconstructs these modifications into non-linear explanations in the original image space. The proposed approach is based on global and local regularisation of the latent space, which stores visual and semantic information about images. Specifically, a multi-objective genetic algorithm is designed for searching explanations, finding individuals that can misclassify the classification output of the network while producing the minimum number of changes in the image descriptor. The genetic algorithm is able to search for explanations without defining any hyperparameters, and uses only one individual to provide a complete explanation of the whole image. Furthermore, the explanations found by the proposed approach are compared with state-of-the-art eXplainable Artificial Intelligence systems and the results show an improvement in the precision of the explanation between 56.39 and 7.23 percentage points.

PMID:39999495 | DOI:10.1016/j.compbiomed.2025.109857

Comput Biol Med. 2025 Feb 24;188:109835. doi: 10.1016/j.compbiomed.2025.109835. Online ahead of print.

ABSTRACT

The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full potential in healthcare. This review examines these challenges and evaluates the effectiveness of AI-IoMT techniques in predicting chronic and terminal diseases, including cardiovascular conditions, Alzheimer's disease, and cancers. We analyze a range of Machine Learning (ML) and Deep Learning (DL) approaches (e.g., XGBoost, Random Forest, CNN, LSTM), alongside advanced strategies like federated learning, transfer learning, and blockchain, to improve model robustness, data security, and interoperability. Findings highlight that transfer learning and ensemble methods enhance model adaptability across clinical settings, while blockchain and federated learning effectively address privacy and data standardization. Ultimately, the review emphasizes the importance of data harmonization, secure frameworks, and multi-disease models as critical research directions for scalable, comprehensive AI-IoMT solutions in healthcare.

PMID:39999492 | DOI:10.1016/j.compbiomed.2025.109835

Hepatology. 2025 Feb 25. doi: 10.1097/HEP.0000000000001283. Online ahead of print.

ABSTRACT

BACKGROUND AIMS: The prognoses and therapeutic responses of patients with intrahepatic cholangiocarcinoma (iCCA) depend on spatial interactions among tumor microenvironment (TME) components. However, the spatial TME characteristics of iCCA remain poorly understood. The aim of this study was to generate a comprehensive spatial atlas of iCCA using artificial intelligence-assisted spatial multiomics patterns and to identify spatial features associated with prognosis and immunotherapy.

APPROACH RESULTS: Spatial multiomics, including imaging mass cytometry (IMC, n=155 in-house), spatial proteomics (n=155 in-house), spatial transcriptomics (n=4 in-house), multiplex immunofluorescence (mIF, n=20 in-house), single-cell RNA sequencing (scRNA-seq, n=9 in-house and n=34 public), bulk RNA-seq (n=244 public), and bulk proteomics (n=110 in-house and n=214 public), were employed to elucidate the spatial TME of iCCA. More than 1.06 million cells were resolved, and the findings revealed that spatial topology, including cellular deposition patterns, cellular communities, and intercellular communications, profoundly correlates with the prognosis of iCCA patients. Specifically, CD163hi M2-like resident-tissue macrophages suppress anti-tumor immunity by directly interacting with CD8+ T cells, resulting in poorer patient survival. Additionally, five spatial subtypes with distinct prognoses were identified, and potential therapeutic options were generated for these subtypes. Furthermore, a spatial TME deep learning system was developed to predict the prognosis of iCCA patients with high accuracy from a single 1-mm2 tumor sample.

CONCLUSIONS: This study offers preliminary insights into the spatial TME ecosystem of iCCA, providing valuable foundations for precise patient classification and the development of personalized treatment strategies.

PMID:39999448 | DOI:10.1097/HEP.0000000000001283

Adv Sci (Weinh). 2025 Feb 25:e2413124. doi: 10.1002/advs.202413124. Online ahead of print.

ABSTRACT

The spatial structure of cells is highly organized at multiscale levels from global spatial domains to local cell type heterogeneity. Existing methods for analyzing spatially resolved transcriptomics (SRT) are separately designed for either domain alignment across multiple slices or deconvoluting cell type compositions within a single slice. To this end, a novel deep learning method, SMILE, is proposed which combines graph contrastive autoencoder and multilayer perceptron with local constraints to learn multiscale and informative spot representations. By comparing SMILE with the state-of-the-art methods on simulation and real datasets, the superior performance of SMILE is demonstrated on spatial alignment, domain identification, and cell type deconvolution. The results show SMILE's capability not only in simultaneously dissecting spatial variations at different scales but also in unraveling altered cellular microenvironments in diseased conditions. Moreover, SMILE can utilize prior domain annotation information of one slice to further enhance the performance.

PMID:39999288 | DOI:10.1002/advs.202413124

Neurodiagn J. 2025 Feb 25:1-11. doi: 10.1080/21646821.2025.2465089. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while addressing interrater variability and the growing data volume. The evolution of AI tools, from early mimetic methods to advanced deep learning techniques, has significantly improved spike and seizure detection in EEG and facilitated whole EEG evaluations, reducing the workload on clinicians. In EMG, AI demonstrates promise in identifying motor unit abnormalities and analyzing audio signals, though challenges persist due to limited datasets and clinical context considerations. PSG scoring has seen substantial integration of AI, with systems achieving high accuracy through uncertainty estimation and selective manual review, but limitations remain in analyzing epileptic activity and classifying certain sleep stages. As a "co-pilot," AI augments human expertise by improving quality control, standardizing clinical trials, and enabling rapid data review, particularly for less experienced providers. Future AI advancements in CNP aim to shift from isolated data interpretation to providing clinical context, considering patient history, treatment options, and prognostic implications. While the potential of generative AI and "AI-omics" is transformative, the importance of thoughtful integration to augment rather than replace human expertise must be emphasized, ensuring that AI becomes a tool for collaboration and innovation in medicine.

PMID:39999187 | DOI:10.1080/21646821.2025.2465089

JMIR Aging. 2025 Feb 25;8:e65221. doi: 10.2196/65221.

ABSTRACT

BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited.

OBJECTIVE: This study aims to (1) determine the time intervals between initial memory loss complaints and dementia diagnosis in outpatient care, (2) assess the proportion of patients receiving cognition-enhancing medication prior to dementia diagnosis, and (3) identify patient and provider characteristics that influence the time between memory complaints and diagnosis and the prescription of cognition-enhancing medication.

METHODS: This retrospective cohort study used a large outpatient electronic health record (EHR) database from the University of Connecticut Health Center, covering 2010-2018, with a cohort of 581 outpatients. We used a customized deep learning-based natural language processing (NLP) pipeline to extract clinical information from EHR data, focusing on cognition-related symptoms, primary caregiver relation, and medication usage. We applied descriptive statistics, linear, and logistic regression for analysis.

RESULTS: The NLP pipeline showed precision, recall, and F1-scores of 0.97, 0.93, and 0.95, respectively. The median time from the first memory loss complaint to dementia diagnosis was 342 (IQR 200-675) days. Factors such as the location of initial complaints and diagnosis and primary caregiver relationships significantly affected this interval. Around 25.1% (146/581) of patients were prescribed cognition-enhancing medication before diagnosis, with the number of complaints influencing medication usage.

CONCLUSIONS: Our NLP-guided analysis provided insights into the clinical pathways from memory complaints to dementia diagnosis and medication practices, which can enhance patient care and decision-making in outpatient settings.

PMID:39999185 | DOI:10.2196/65221

PLoS One. 2025 Feb 25;20(2):e0319056. doi: 10.1371/journal.pone.0319056. eCollection 2025.

ABSTRACT

Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information processing, elevate medical service quality, and aid clinical decision-making. Nonetheless, current methods exhibit limitations in contextual awareness and insufficient consideration of contextual relevance and interactions between entities. In this study, we initially encode medical text inputs using the Chinese pre-trained RoBERTa-wwm-ext model to extract comprehensive contextual features and semantic information. Subsequently, we employ recurrent neural networks in conjunction with the multi-head attention mechanism as the primary gating structure for parallel processing and capturing inter-entity dependencies. Finally, we leverage conditional random fields in combination with the cross-entropy loss function to enhance entity recognition accuracy and ensure label sequence consistency. Extensive experiments conducted on datasets including MCSCSet and CMeEE demonstrate that the proposed model attains F1 scores of 91.90% and 64.36% on the respective datasets, outperforming other related models. These findings confirm the efficacy of our method for recognizing named entities in Chinese medical texts.

PMID:39999103 | DOI:10.1371/journal.pone.0319056

PLoS One. 2025 Feb 25;20(2):e0317900. doi: 10.1371/journal.pone.0317900. eCollection 2025.

ABSTRACT

Cardiovascular disease is one of the most dangerous conditions, posing a significant threat to daily health. Electrocardiography (ECG) is crucial for heart health monitoring. It plays a pivotal role in early heart disease detection, heart function assessment, and guiding treatments. Thus, refining ECG diagnostic methods is vital for timely and accurate heart disease diagnosis. Recently, deep learning has significantly advanced in ECG signal classification and recognition. However, these methods struggle with new or Out-of-Distribution (OOD) heart diseases. The deep learning model performs well on existing heart diseases but falters on unknown types, which leads to less reliable diagnoses. To address this challenge, we propose a novel trustworthy diagnosis method for ECG signals based on OOD detection. The proposed model integrates Convolutional Neural Networks (CNN) and Attention mechanisms to enhance feature extraction. Meanwhile, Energy and ReAct techniques are used to recognize OOD heart diseases and its generalization capacity for trustworthy diagnosis. Empirical validation using both the MIT-BIH Arrhythmia Database and the INCART 12-lead Arrhythmia Database demonstrated our method's high sensitivity and specificity in diagnosing both known and out-of-distribution (OOD) heart diseases, thus verifying the model's diagnostic trustworthiness. The results not only validate the effectiveness of our approach but also highlight its potential application value in cardiac health diagnostics.

PMID:39999066 | DOI:10.1371/journal.pone.0317900

J Clin Gastroenterol. 2025 Feb 19. doi: 10.1097/MCG.0000000000002148. Online ahead of print.

ABSTRACT

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures.

METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC).

RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively.

CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

PMID:39998988 | DOI:10.1097/MCG.0000000000002148

Rheumatology (Oxford). 2025 Feb 25:keaf108. doi: 10.1093/rheumatology/keaf108. Online ahead of print.

ABSTRACT

OBJECTIVES: Antineutrophil cytoplasmic antibodies (ANCAs) are occasionally positive in patients with interstitial lung disease (ILD). The positivity rates of ANCAs in various types of ILD and the role of ANCAs in ILD are still unclear. The purpose of this study was to estimate the prevalence of ANCAs in Chinese people diagnosed with ILD (including idiopathic pulmonary fibrosis) and identify differences in clinical features, radiographic features, and survival between patients with ANCA-positive and ANCA-negative ILD.

METHODS: We retrospectively reviewed the data of 706 ILD patients with available ANCA results from March 2010 to October 2023 at the First Affiliated Hospital of Ningbo University. Patient demographics, symptoms, laboratory parameters, chest CT, and pulmonary function testing results were collected and analysed at each patient's initial diagnosis. The prevalence and associations of ANCAs with clinical characteristics and survival were evaluated.

RESULTS: ANCAs were positive in 158 of the 706 (22.4%) ILD patients. Compared with ANCA-negative ILD patients, ANCA-positive ILD patients tended to be older, had higher CRP and ESR levels, and had a significantly greater proportion of rheumatoid factor positivity. In total, 58.2% (92/158) of patients were ANCA-positive on average (41.6 ± 31.4) months after ILD diagnosis. Patients with ANCA-positive ILD had higher all-cause mortality than did those with ANCA-negative ILD (33.5% vs 25.0%, p = 0.033). The usual interstitial pneumonia (UIP) pattern (56.3%) was the most common chest HRCT pattern. The proportions of patients with honeycombing (p < 0.001) and oddly shaped cysts (p < 0.001) were significantly greater in the ANCA-positive ILD group than in the ANCA-negative ILD group. Acute exacerbation (AE) of ILD (HR 2.40, 95% CI 1.37-4.22, p = 0.002) was independently associated with shorter survival, and receiving glucocorticoids combined with immunosuppressants (HR 0.30, 95% CI 0.16-0.57, p < 0.001) was independently associated with longer survival in ANCA-positive ILD patients.

CONCLUSIONS: The prevalence of ANCAs in patients with ILD is not rare, and ANCA testing in ILD patients is necessary. Oddly shaped cysts with or without a UIP pattern may be a characteristic chest imaging manifestation of ANCA-positive ILDs. The frequency of AEs in ANCA-positive ILD patients is high, and more attention should be given to ANCA-positive ILD patients who have AEs.

PMID:39999033 | DOI:10.1093/rheumatology/keaf108

Hepatology. 2025 Feb 25. doi: 10.1097/HEP.0000000000001281. Online ahead of print.

ABSTRACT

Drug-induced liver injury (DILI) is rare in clinical practice but when it occurs it can lead to acute liver failure and death. Drug developers and regulators undertake a series of steps to identify the DILI potential of a medication before it is approved for marketing. Preclinical testing by drug developers typically involves a multitude of in vitro assays and in vivo animal experiments before a compound is moved into first-in-human phase 1 testing. Over the last two decades, there have been a number of advances in preclinical screening for DILI potential of a new chemical entity, but these approaches tend to be overly sensitive with insufficient positive predictive value. Once in clinical trials, the DILI potential of an investigational agent and risks to a participant are carefully managed through patient selection, DILI monitoring paradigms, and drug interruption and discontinuation criteria, in close concert with the regulators. Recent developments in Quantitative Systems Toxicology offer promising and complementary in silico approaches to predict the compound's risk for DILI via multifaceted systems biology. When a drug developer submits a New Drug Application (NDA) for marketing approval, regulators review the preclinical and clinical trial data in a structured fashion to assess the DILI risk. While these approaches have been successful in dramatically reducing the marketing approval of medications eventually associated with hepatotoxicity, many challenges remain in identifying the risk for DILI during preclinical and early-to-late clinical development stages for genetic medicines, biological agents, and immunotherapies. In this review, we discuss current preclinical, in-silico, and clinical development approaches to screen for DILI potential of an investigational agent and provide a high-level description of regulators' approach for assessing DILI risk in an NDA.

PMID:39999469 | DOI:10.1097/HEP.0000000000001281

Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2415933122. doi: 10.1073/pnas.2415933122. Epub 2025 Feb 25.

ABSTRACT

Atherosclerosis and vessel wall trauma induce vascular smooth muscle cell (VSMC) phenotypic modulation, leading to plaque cap growth and postintervention restenosis. Our systems biology approach identified RNA binding protein, mRNA processing factor (RBPMS) as a conserved, VSMC-specific gene associated with VSMC modulation in atherosclerosis. RBPMS gene expression positively correlates with VSMC contractile markers in human and murine atherosclerotic arteries as well as in two vascular injury models during the postinjury intimal hyperplasia phase. RBPMS promotes contractile VSMC differentiation, reduces plaque cap development in high-fat diet-fed apolipoprotein E-null (ApoE-/-) murine atherosclerotic arteries, and inhibits intimal hyperplasia. Mechanistically, the RBPMS protein interacts with the myocardin (MYOCD) pre-mRNA and enhances MYOCD_v3/MYOCD_v1 transcript balance through alternative exon 2a splicing. RBPMS promotes the VSMC contractile phenotype and reduces their fibroproliferative activity in a MYOCD_v3a-dependent manner. RBPMS enhances Myocd_v3/Myocd_v1 transcript balance in both atherosclerotic and injured vessels. RBPMS may inhibit VSMC-driven plaque cap development and intervention-induced restenosis.

PMID:39999164 | DOI:10.1073/pnas.2415933122

PLoS One. 2025 Feb 25;20(2):e0314970. doi: 10.1371/journal.pone.0314970. eCollection 2025.

ABSTRACT

Assessing the potential for oseltamivir-induced liver damage is essential to ensure its safe administration. The aim of this study was to examine the association between hepatotoxicity and oseltamivir use and to describe the features of oseltamivir-induced hepatotoxicity. Data were obtained from the Adverse Event Reporting System of the US Food and Drug Administration (FAERS). Disproportionality and proportionality analyses were performed to evaluate the safety profile of oseltamivir-related hepatotoxicity and the occurrence of hepatotoxicity-related adverse events across sex and age groups. The FAERS recorded 20,340,254 adverse event reports between 2004 and 2023, of which 16,960,996 reports were included in the analysis. We identified 14 types of oseltamivir-related adverse events that were hepatotoxic and showed positive signals. The most frequently reported adverse event was abnormal hepatic function (n = 54), and the most severe adverse event was fulminant hepatitis. Compared with that for male individuals, the reporting odds ratio (ROR) was 0.5 for female individuals; and for male individuals, the ROR, compared with that for female individuals, was 4.19. The median time to hepatotoxic adverse events, excluding mixed liver injury, was < 5 days. Oseltamivir can cause liver toxicity, which is influenced by sex and age. Liver function tests and monitoring for signs of liver disease are crucial when using oseltamivir.

PMID:39999160 | DOI:10.1371/journal.pone.0314970

PLoS One. 2025 Feb 25;20(2):e0318597. doi: 10.1371/journal.pone.0318597. eCollection 2025.

ABSTRACT

Serious Adverse Drug Reactions (ADRs) can cause a longer stay, which can result in fatal outcomes. Understanding the prognostic factors for the serious ADRs play a vital role in developing appropriate serious ADR prevention strategies. This study aimed to analyze nationwide database in Thailand to identify predisposing factors associated with the serious ADRs, explore drug exposure, distribution of serious ADRs, types of ADRs, and classify the determinants of serious ADR due to anti-infective in Thailand. The national database of anti-infective-induced ADRs from January 2012 to December 2021 in Thailand's 77 provinces, Thai Vigibase at the Health Product Vigilance Center (HPVC), was considered. After pre-processing, frequencies and percentages were used to investigate the distribution of ADR seriousness. To determine the significance of the independent variables on the seriousness of anti-infective-induced ADRs, logistic regression and the Classification and Regression Tree (CART) model were performed. A p-value < 0.05 was considered statistically significant. A total of 82,333 ADR cases, of which 20,692 were serious ADRs (25.13%). Serious ADRs is statistically associated with region, gender, ethnicity, age, type of patient, history of drug allergy, chronic disease and dose frequency (p-value < 0.001). The most commonly reported serious ADRs were in the South region of Thailand (OR = 1.92, 95% CI = 1.88-1.97), followed by the North region (OR = 1.68, 95% CI = 1.64-1.71) of Thailand. Gender and history of drug allergy were also statistically associated with the seriousness of ADRs (p-value = 0.001). Reported ADRs revealed that patients were males (OR = 1.11, 95% CI = 1.11-1.13) and those with a prior history of drug allergy (OR = 1.22, 95% CI = 1.20-1.24) were more likely to experience serious ADRs. The risk of having an ADR reported as serious was significantly higher in patients aged 60 and over (OR = 1.42, 95% CI = 1.39-1.46) and patients aged 40-59 years (OR = 1.34, 95% CI = 1.31-1.37) compared to patients aged 0-19 years. IPD patients most commonly associated with serious ADRs. The results of this study will enable healthcare professionals to use caution when prescribing to those groups. Furthermore, developing a reporting system to reduce serious ADR evidence, such as software with electronic prescribing databases or applications that enable efficient detection of ADRs in high-risk groups, was critical in order to closely monitor and improve patient safety.

PMID:39999099 | DOI:10.1371/journal.pone.0318597

Interdiscip Sci. 2025 Feb 25. doi: 10.1007/s12539-025-00692-9. Online ahead of print.

ABSTRACT

Predicting drug-target binding affinity (DTA) is an important step in the complex process of drug discovery or drug repositioning. A large number of computational methods proposed for the task of DTA prediction utilize single features of proteins to measure drug-protein or protein-protein interactions, ignoring multi-feature fusion between protein-related features (e.g., solvent accessibility, protein pockets, secondary structures, and distance maps, etc.). To address the aforementioned constraints, we propose a new network topology and multi-feature fusion based approach for DTA prediction (NTMFF-DTA), which deeply mines protein multiple types of data and propagates drug information across domains. Data in drug-target interactions are often sparse, and multi-feature fusion can enrich data information by integrating multiple features, thus overcoming the data sparsity problem to some extent. The proposed approach offers two main contributions: (1) constructing a relationship-aware GAT that selectively focuses on the connections between nodes and edges in the molecular graph to capture the more central roles of nodes and edges in DTA prediction and (2) constructing an information propagation channel between different feature domains of drug proteins to achieve the sharing of the importance weight of drug atoms and edges, and combining with a multi-head self-attention mechanism to capture residue-enhancing features. The NTMFF-DTA model was comparatively tested against several leading baseline technologies on commonly used datasets. Experimental show that NTMFF-DTA can effectively and accurately predict DTA and outperform existing comparative models.

PMID:39998589 | DOI:10.1007/s12539-025-00692-9

J Fungi (Basel). 2025 Feb 10;11(2):133. doi: 10.3390/jof11020133.

ABSTRACT

Invasive candidiasis is a common fungal infection associated with multiple risk factors, such as cancer, neutropenia, corticosteroid therapy, catheterization, and the use of broad-spectrum antibiotic treatment. Candida albicans is the predominant causative agent, although other Candida species have been emerging in the last years, together with a rise in a number of strains resistant to the currently available antifungal drugs, which poses a challenge when treating these infections. Drug repurposing and drug combinations are promising strategies for the treatment of invasive mycoses. In this study, we evaluated the effect of the combination of fluconazole (FLZ) and cyclosporine A (CsA) against 39 clinical isolates and reference strains of Candida. Two methods, the Loewe additivity model and Bliss independence model, were used to assess the antifungal activity of the drug combination according to CLSI and EUCAST guidelines. The results demonstrated a synergistic effect between fluconazole (FLZ) and cyclosporine A (CsA) against 15-17 Candida isolates, depending on the evaluation model used, including FLZ-resistant strains of C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis. Notably, the combination significantly reduced the minimum inhibitory concentration (MIC) of FLZ in a substantial number of isolates, including those with resistance to FLZ. Additionally, time-kill curve studies confirmed the synergistic interaction, further validating the potential of this combination as an alternative therapeutic strategy for candidiasis treatment. These findings emphasize the importance of investigating innovative drug combinations to address the challenges posed by antifungal resistance and improve treatment options for invasive fungal infections.

PMID:39997427 | DOI:10.3390/jof11020133

Cells. 2025 Feb 15;14(4):287. doi: 10.3390/cells14040287.

ABSTRACT

Patients with treatment-refractory/relapsing germ cell tumors (GCTs) have a dismal prognosis due to a lack of any effective therapy. Moreover, the efficacy of newly approved targeted therapies remains unexplored for cisplatin-resistant GCTs. Previously, it was demonstrated that folate receptor α (FRα) is overexpressed in many tumor types and efficiently targeted by the antibody-drug conjugate (ADC) mirvetuximab soravtansine (MIRV) in cisplatin-resistant cancers. We hypothesized that FRα represents an attractive target for treating treatment-refractory GCTs. We determined the expression of the FOLR1 gene in a broad range of GCT cell lines and tumor xenografts. We tested the antitumor efficacy of MIRV on cisplatin-resistant GCT cells in vitro and explored the ability of MIRV treatment to induce a bystander effect in the direct coculture of FRα-high and FRα-low cells. We found that the FOLR1 gene has significantly higher expression in testicular GCTs (TGCTs) than in normal testicular tissue. FOLR1 is highly expressed in the TCam2, JEG3, JAR, and NOY1 cell lines and their respective cisplatin-resistant variants. MIRV treatment induced apoptosis and a potent antiproliferative effect in cisplatin-resistant GCT cells in adherent and 3D spheroid cultures in vitro. A significant decrease in FRα-low 2102EP_R_NL cells was observed in the presence of FRα-high NOY1_R_SK in the presence of 12.5 nM MIRV, showing a potent bystander effect in the direct coculture. Immunohistochemical analysis confirmed significantly higher Folr1 protein expression in patients with TGCTs postchemotherapy than in chemo-naïve patients, as well as in patients with an unfavorable prognosis. In this study, we present data suggesting that the FOLR1 gene is highly expressed in (T)GCT cells in vitro and in vivo, and anti-FRα-targeting therapies should be investigated as a treatment modality in a subset of patients with TGCTs. Moreover, MIRV induced significant antitumor and bystander effects, thus showing its potential in further preclinical exploration and drug repurposing for a salvage treatment regime in refractory (T)GCT disease.

PMID:39996761 | DOI:10.3390/cells14040287