Cureus. 2025 Apr 3;17(4):e81664. doi: 10.7759/cureus.81664. eCollection 2025 Apr.

ABSTRACT

Hydroxychloroquine (HCQ) is a medication that is commonly used as an antimalarial or disease-modifying anti-rheumatic drug (DMARD). Adverse side effects typically include retinal damage, cardiomyopathy, and neuromyopathy. However, there has been relatively little documentation on the effects of HCQ toxicity on the liver. We describe a case of HCQ-induced hepatic failure in a 31-year-old female patient on HCQ for systemic lupus erythematous who presented with three days of fever, diarrhea, and non-bilious, non-bloody vomiting. Labs showed massively elevated liver function tests (LFTs) and negative viral serology. The abdominal ultrasound and magnetic resonance cholangiopancreatography were unremarkable. A liver biopsy showed portal tracts with mild to moderate expansion by mixed inflammatory infiltrates, including scattered eosinophils and rare plasma cells with occasional mild interface activity focally close to bridging inflammation. These findings are consistent with drug-induced liver injury (DILI). HCQ was subsequently held, and the patient was admitted to the ICU for conservative management. Repeat LFTs showed down-trending over the course of the patient's four days of admission after discontinuation of HCQ and returned to baseline within a two-month timeframe.

PMID:40322400 | PMC:PMC12049183 | DOI:10.7759/cureus.81664

Expert Opin Drug Saf. 2025 May 5. doi: 10.1080/14740338.2025.2500716. Online ahead of print.

ABSTRACT

BACKGROUND: Non-infectious pneumonitis (NIP) is a severe adverse drug reaction. To better understand drug-induced NIP, improve patient safety, and inform clinical decision-making, this study aims to utilize the FDA Adverse Event Reporting System (FAERS) database to evaluate the association between medications and NIP, identify potential risk factors, and offer clinical alerts.

RESEARCH DESIGN AND METHODS: We reviewed the FAERS database from the 2004 through the second quarter of 2024. Using 'NIP' as the search term, we sorted, counted, and analyzed cases by generic drug name and trends of reports related to NIP submitted to FAERS database. We employed four statistical methods to identify drugs associated with the NIP.

RESULTS: From 21,433,114 reported drug adverse events (AEs), 9,224 cases were classified as NIP. Our analysis identified 20 drugs associated with NIP, with the main categories being antineoplastic agents, antibiotics and immunosuppressants. Daptomycin, methotrexate, and tacrolimus had the highest NIP-related deaths. Trends in AEs reporting indicate that the drugs showing the fastest increase in NIP reports are daptomycin, methotrexate, sertraline, and amiodarone.

CONCLUSION: These findings could assist clinicians in the early identification of drug-related NIP and provide valuable insights for future research into the mechanisms underlying drug-related NIP.

PMID:40321104 | DOI:10.1080/14740338.2025.2500716

Water Res. 2025 Apr 22;282:123711. doi: 10.1016/j.watres.2025.123711. Online ahead of print.

ABSTRACT

Urban flood control and drainage systems often face significant challenges in coordinating municipal drainage with river-lake flood prevention during flood seasons. Rising river levels can create backwater effects, which substantially increase urban flood risks. Traditional water management approaches are limited by delayed monitoring data updates, slow flood forecasting processes, and inadequate decision support, making it difficult to address the complex, multi-objective demands of flood control. These limitations exacerbate flooding threats and hamper effective urban flood management. To address these challenges, a digital twin experimental platform for river and lake water systems was developed to enhance the comprehensive management of urban flood control and drainage. The platform integrates an engineering entity, a backend system, and a digital twin component. Real-time data acquisition and virtual-real interactions between physical facilities and the digital twin were achieved using Programmable Logic Controller (PLC) technology, while the Unity3D engine enabled advanced visualization and data rendering. Furthermore, a novel model incorporating deep learning and a multi-objective optimization algorithm was proposed to optimize drainage pump scheduling rules. A comparative analysis was conducted to evaluate flood risks and operation and maintenance costs before and after optimization. The results demonstrated that the platform was well-designed for comprehensive flood protection and drainage management. The NSE coefficients for river and lake water levels exceeded 95.18 %, and the relative error in pump operation times remained below 4.11 % across various scenarios involving river inflows and drainage operations. The backwater effect at drainage outlets was primarily driven by river flow and downstream lake levels. The optimization strategy effectively balanced water level control and operational objectives, reducing water level targets by 24.99 %, 40.36 %, and 51.61 % under different scenarios. This framework not only offers innovative solutions for urban flood management but also provides strong technical support for optimizing flood control and drainage system operations.

PMID:40319783 | DOI:10.1016/j.watres.2025.123711

Comput Biol Med. 2025 May 3;192(Pt A):110279. doi: 10.1016/j.compbiomed.2025.110279. Online ahead of print.

ABSTRACT

BACKGROUND: Oral health plays a vital role in our daily lives, affecting essential activities like eating, speaking, and smiling. Poor oral health can lead to significant social, psychological, and physical consequences, which makes early and accurate diagnosis incredibly important. Recent advances in artificial intelligence (AI) are opening new doors in oral health care, offering faster, more accurate ways to identify dental issues and improve overall care.

METHODS: This paper uses RGB color photography to introduce a non-linear Fuzzy Rank-based Ensemble Deep Learning model (NFR-EDL) for diagnosing oral and dental diseases. The model utilizes four deep Convolutional Neural Network (CNN) base models to analyze high-resolution color images of the oral cavity. The CNN base models are initially trained to generate confidence scores, which are subsequently mapped onto distinct functions with varying concavities, resulting in non-linear fuzzy ranks. Then, these ranks are combined into a final score to minimize the deviation from expected results. This method aims to provide accurate, reliable identification of oral and dental disease diagnosis by fusing many base models and considering uncertainty in decision-making while utilizing the rich visual information available in RGB images.

RESULTS: The experimental results demonstrate that the proposed NFR-EDL model achieves accuracies of 97.08 %, 84.00 %, 89.86 %, and 94.66 % on the Kaggle, MOD, ODSI-DB, and OaDD datasets, respectively. These results demonstrate the model's exceptional accuracy and effectiveness in diagnosing oral and dental diseases, outperforming existing techniques and enhancing diagnostic reliability in clinical settings.

CONCLUSION: Deploying the NFR-EDL model in clinical settings offers a highly accurate and reliable tool for diagnosing oral and dental diseases, enhancing early detection, personalizing patient care, and reducing diagnostic errors to ultimately improve patient outcomes and the efficiency of dental care delivery. This approach reduces uncertainty in decision-making, ensuring that diagnoses are made with high confidence.

PMID:40319757 | DOI:10.1016/j.compbiomed.2025.110279

Biomed Pharmacother. 2025 May 3;187:118124. doi: 10.1016/j.biopha.2025.118124. Online ahead of print.

ABSTRACT

Chronic kidney disease (CKD) is characterized by kidney fibrosis involving epithelial-mesenchymal transition (EMT), and extracellular matrix (ECM) accumulation, and often leads to end-stage kidney disease (ESKD). Currently, available therapies are not uniformly effective and lead to serious adverse effects. Levosimendan (LVS), a calcium sensitizer and an inodilator, manages cardiac failure. We aimed to evaluate the renoprotective effect of LVS on unilateral ureteral obstruction (UUO)-induced CKD in male Sprague-Dawley (SD) rats and exogenous transforming growth factor-β1 (TGF-β1)-induced fibrosis in NRK-52E cells. Rats were randomly grouped as normal control (NC), sham, UUO and UUO + LVS (3 mg/kg, p.o., o.d.) for 21 days. All animals were sacrificed post-treatment, and plasma, urine and kidney specimens were utilized for biochemistry, histology, immunohistochemistry and immunoblotting. Moreover, exogenous TGF-β1 was used to stimulate kidney fibrosis in NRK-52E cells and treated with LVS (10 µM) for 48 h. The in-vitro samples were collected for cell morphology, viability, immunofluorescence and immunoblotting. LVS treatment significantly improved the kidney mass, plasma and urine creatinine, BUN, urine urea nitrogen and plasma proteins levels of TGF-β1 and fibronectin. Histology revealed a significant decrease in tubular necrosis, glomerulosclerosis and tubulointerstitial fibrosis in LVS-treated rats. Moreover, LVS treatment remarkably downregulated the levels of α-SMA, vimentin, p-Smad 2/3 and upregulated E-cadherin in UUO rats, decreased Smad 4, collagen I, β-catenin, and MMP-7-mediated ECM and increased Smurf 2 and Smad 7 in NRK-52E cells. LVS inhibits EMT and ECM turnover via TGF-β1/Smad axis modulation, highlighting the potential clinical use of LVS for CKD.

PMID:40319657 | DOI:10.1016/j.biopha.2025.118124

Mol Cell Pediatr. 2025 May 5;12(1):6. doi: 10.1186/s40348-025-00194-0.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a systemic disorder of exocrine glands that is caused by mutations in the CFTR gene.

MAIN BODY: The basic defect in people with CF (pwCF) leads to impaired epithelial transport of chloride and bicarbonate that can be assessed by CFTR biomarkers, i.e. the β-adrenergic sweat rate and sweat chloride concentration (SCC), chloride conductance of the nasal respiratory epithelium (NPD), urine secretion of bicarbonate, intestinal current measurements (ICM) of chloride secretory responses in rectal biopsies and in bioassays of chloride transport in organoids or cell cultures. CFTR modulators are a novel class of drugs that improve defective posttranslational processing, trafficking and function of mutant CFTR. By April 2025, triple combination therapy with the CFTR potentiator ivacaftor (IVA) and the CFTR correctors elexacaftor (ELX) and tezacaftor (TEZ) has been approved in Europe for the treatment of all pwCF who do not carry two minimal function CFTR mutations. Previous phase 3 and post-approval phase 4 studies in pwCF who harbour one or two alleles of the major mutation F508del consistently reported significant improvements of lung function and anthropometry upon initiation of ELX/TEZ/IVA compared to baseline. Normalization of SCC, NPD and ICM correlated with clinical outcomes on the population level, but the restoration of CFTR function was diverse and not predictive for clinical outcome in the individual patient. Theratyping of non-F508del CF genotypes in patient-derived organoids and cell cultures revealed for most cases clinically meaningful increases of CFTR activity upon exposure to ELX/TEZ/IVA. Likewise, every second CF patient with non-F508del genotypes improved in SCC and clinical outcome upon exposure to ELX/TEZ/IVA indicating that triple CFTR modulator therapy is potentially beneficial for all pwCF who do not carry two minimal function CFTR mutations. This group who is not eligible for CFTR modulators may opt for gene addition therapy in the future, as the first-in-human trial with a recombinant lentiviral vector is underway.

FUTURE DIRECTIONS: The upcoming generation of pwCF will probably experience a rather normal life in childhood and adolescence. To classify the upcoming personal signatures of CF disease in the times of efficient modulators, we need more sensitive CFTR biomarkers that address the long-term course of airway and gut microbiome, host defense, epithelial homeostasis and multiorgan metabolism.

PMID:40320452 | DOI:10.1186/s40348-025-00194-0

J Cyst Fibros. 2025 May 3:S1569-1993(25)01465-1. doi: 10.1016/j.jcf.2025.04.010. Online ahead of print.

ABSTRACT

BACKGROUND: Despite widespread availability of modulator therapies and improved lung function in many people with cystic fibrosis (CF), physical symptoms may remain burdensome for some people with CF (PwCF). This study identifies the impact of ivacaftor (IVA) and elexacaftor/tezacaftor/ivacaftor (ETI) on self-reported physical well-being and burden of care among adults with CF.

METHODS: We conducted a secondary analysis of data from the Well-ME Survey. Participants included adults with CF (age≥18) who reported taking IVA or ETI. We used a mixed methods approach to identify self-reported health status, physical well-being, and experience of CF care while taking IVA or ETI.

RESULTS: Among 414 eligible respondents, overall health status was reported very good/excellent by 59 % (n = 243), good by 26 % (n = 114), and poor/fair by 14 % (n = 57). While the majority of respondents experienced improvements in respiratory symptoms, PwCF reporting poor/fair health were less likely to report improvement in overall physical health, fatigue, and ability to exercise compared to those with good or very good/excellent health and less likely to report improvement in pain, sinus issues, and cough than those with very good/excellent health. PwCF reporting poor/fair health or good health were less likely to report improvements in gastrointestinal issues or experience reductions in CF medications or treatments, compared to those reporting very good/excellent health.

CONCLUSIONS: Despite improvements in respiratory symptoms, some adults with CF taking IVA or ETI report their health is poor/fair. A better understanding of physical well-being and burden of care may help identify underrecognized comorbidities to improve care.

PMID:40320360 | DOI:10.1016/j.jcf.2025.04.010

J Cheminform. 2025 May 4;17(1):67. doi: 10.1186/s13321-025-01019-y.

ABSTRACT

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database.Scientific contribution: Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

PMID:40320551 | DOI:10.1186/s13321-025-01019-y

Sci Rep. 2025 May 4;15(1):15602. doi: 10.1038/s41598-025-97656-2.

ABSTRACT

Mobile robots are used in various fields, from deliveries to search and rescue applications. Different types of sensors are mounted on the robot to provide accurate navigation and, thus, allow successful completion of its task. In real-world scenarios, due to environmental constraints, the robot frequently relies only on its inertial sensors. Therefore, due to noises and other error terms associated with the inertial readings, the navigation solution drifts in time. To mitigate the inertial solution drift, we propose the MoRPINet framework consisting of a neural network to regress the robot's travelled distance. To this end, we require the mobile robot to maneuver in a snake-like slithering motion to encourage nonlinear behavior. MoRPINet was evaluated using a dataset of 290 minutes of inertial recordings during field experiments and showed an improvement of 33% in the positioning error over other state-of-the-art methods for pure inertial navigation.

PMID:40320468 | DOI:10.1038/s41598-025-97656-2

Sci Rep. 2025 May 5;15(1):15616. doi: 10.1038/s41598-025-91518-7.

ABSTRACT

Rockburst is a significant safety threat in coal mining, influenced by complex nonlinear dynamic characteristics and multi-factor coupling. This study proposes a rockburst risk prediction method based on the SSA-CNN-MoLSTM-Attention model. The model integrates the local feature extraction capability of convolutional neural networks (CNN), the temporal modeling advantages of the modified long short-term memory network (MoLSTM), and the enhanced feature recognition capability of the attention mechanism. Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model's performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. The model outperforms traditional methods in mean absolute error (MAE) and root mean square error (RMSE), providing effective insights and a reference for rockburst risk assessment and disaster prevention in mining operations.

PMID:40320457 | DOI:10.1038/s41598-025-91518-7

Sci Rep. 2025 May 4;15(1):15594. doi: 10.1038/s41598-025-00546-w.

ABSTRACT

This study aims to conduct a comprehensive and in-depth analysis of the marketing performance of e-commerce live broadcast platforms based on big data management technology and deep learning. Firstly, by synthesizing large-scale datasets and surveys, the study constructs a series of performance evaluation indicators including user participation, content quality, commodity sales effect, user satisfaction, and platform promotion effect. Secondly, the weight of each indicator is finally determined through the indicator screening of the expert scoring method. Finally, the experimental design and implementation steps such as data collection, experimental environment setting, parameter setting, and performance evaluation are introduced in detail. Through the training and evaluation of the Back Propagation Neural Network (BPNN), each secondary indicator's adjusted weight value and global ranking are obtained, providing a scientific basis for subsequent management opinions. The research results emphasize the importance of comments and ratings, purchase conversion rate, advertising click-through rate, and other indicators in improving user satisfaction, promoting sales, and effective promotion. Overall, this study provides a clear direction for an e-commerce live broadcast platform to optimize user experience, improve sales performance, and strengthen brand promotion.

PMID:40320449 | DOI:10.1038/s41598-025-00546-w

Sci Rep. 2025 May 4;15(1):15614. doi: 10.1038/s41598-025-00516-2.

ABSTRACT

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models' performance when applied to actual clinical datasets is still an open question; this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection's accuracy, sensitivity, and specificity compared to the previous approaches.

PMID:40320438 | DOI:10.1038/s41598-025-00516-2

Sci Rep. 2025 May 4;15(1):15608. doi: 10.1038/s41598-025-00563-9.

ABSTRACT

White blood cell (WBC) classification is a crucial step in assessing a patient's health and validating medical treatment in the medical domain. Hence, efficient computer vision solutions to the classification of WBC will be an effective aid to medical practitioners. Computer-aided diagnosis (CAD) reduces manual intervention, avoids errors, speeds up medical analysis, and provides accurate medical reports. Though a lot of research has been taken up to develop deep learning models for efficient classification of WBCs, there is still scope for improvement to support the data insufficiency issue in medical data sets. Data augmentation and normalization techniques increase the quantity of data but don't enhance the quality of the data. Hence, deep learning models though performing well can still be made efficient and effective when quality data is fused along with the available image dataset. This paper aims to utilize domain knowledge and image data to improve the classification performance of pre-trained models namely Inception V3, DenseNet 121, ResNet 50, MobileNet V2, and VGG 16. The models performance, with and without domain knowledge infused, is analyzed on the BCCD and LISC datasets. On the BCCD dataset, the average accuracies increased from 82.7%, 98.8%, 98.38%, 98.56%, and 98.5%-99.38%, 99.05%, 99.05%, 98.67%, and 98.75% for Inception V3, DenseNet 121, ResNet 50, MobileNet V2, and VGG 16, respectively. Similarly, on the LISC dataset, the accuracies improved from 86.76%, 92.2%, 91.76%, 92.8%, and 94.4%-92.05%, 95.88%, 95.58%, 95.2%, and 95.2%, respectively.

PMID:40320432 | DOI:10.1038/s41598-025-00563-9

Network. 2025 May 4:1-35. doi: 10.1080/0954898X.2025.2500046. Online ahead of print.

ABSTRACT

Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system's effectiveness. This article presents a ResNet framework optimized using Henry gas solubility optimization (HGSO) for the classification of brain tumours, resulting in improved classification performance in magnetic resonance images (MRI). Two variants of the deep residual neural network, namely ResNet-18 and ResNet-50, are trained on the MRI training dataset. The four critical hyperparameters of the ResNet model: momentum, initial learning rate, maximum epochs, and validation frequency are tuned to obtain optimal values using HGSO algorithm. Subsequently, the optimized ResNet model is evaluated using two separate databases: Database1, comprising four tumour classes, and Database2, with three tumour classes. The performance is assessed using accuracy, sensitivity, specificity, precision, and F-score. The highest classification accuracy of 0.9825 is attained using the proposed optimized ResNet-50 framework on Database1. Moreover, the Gradient-weighted Class Activation Mapping (GRAD-CAM) algorithm is utilized to enhance the understanding of deep neural networks by highlighting the regions that are influential in making a particular classification decision. Grad-CAM heatmaps confirm the model focuses on relevant tumour features, not image artefacts. This research enhances MRI brain tumour classification via deep learning optimization strategies.

PMID:40320295 | DOI:10.1080/0954898X.2025.2500046

Bone. 2025 May 2:117510. doi: 10.1016/j.bone.2025.117510. Online ahead of print.

ABSTRACT

OBJECTIVES: To establish a precise and efficient diagnostic framework for distinguishing medication-related osteonecrosis of the jaw, radiation-induced osteonecrosis of the jaw, and normal bone tissue, thus enhancing clinical decision-making and enabling targeted therapeutic interventions.

METHODS: Raman spectroscopy was applied to investigate bone mineral composition, organic matrix content, and crystallinity in ninety bone tissue samples (30 MRONJ, 30 ORN, 30 control). Each mandible underwent 10 randomized spectral acquisitions, yielding 900 spectra across 200-2200 cm-1. The raw spectral data were preprocessed using Labspec6 software (Horiba Scientific). Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed for feature extraction and classification. Additionally, a ResNet18 deep learning architecture was employed to enhance diagnostic accuracy. The model's performance was evaluated using precision, recall, and the area under the receiver operating characteristic curve to ensure robustness.

RESULTS: The PCA-LDA integration achieved 90.3 % accuracy in differentiating MRONJ, ORN, and healthy bone， with leave-one-out cross-validation confirming 89.1 % classification robustness. Furthermore, the ResNet18 deep learning model outperformed traditional classification methods, achieving 0.926 ± 0.024 accuracy, 0.924 ± 0.026 precision, 0.926 ± 0.024 recall, and 0.985 ± 0.007 AUROC on the validation set.

SIGNIFICANCE: These findings underscore the significant potential of combining Raman spectroscopy with advanced deep learning techniques as a rapid, noninvasive, and highly reliable diagnostic tool. This approach not only enhances the ability to differentiate between MRONJ and ORN but also offers substantial implications for improving patient management and therapeutic outcomes in clinical practice.

PMID:40320103 | DOI:10.1016/j.bone.2025.117510

Int J Biol Macromol. 2025 May 2:143844. doi: 10.1016/j.ijbiomac.2025.143844. Online ahead of print.

ABSTRACT

The insulin receptor (IR) is a transmembrane protein that controls glucose homeostasis and is highly associated with chronic diseases including cancer and neurological. Traditional experimental methods have provided essential insights into IR structure and function, but they are constrained by time, cost, and scalability. To address these limitations, we present a computational technique for IR prediction based on deep learning and multi-information fusion. First, we built sequence-based training and testing datasets. Second, the compositional, word embedding, and evolutionary features were retrieved using the Weighted-Group Dipeptide Composition (W-GDPC), FastText, and Bi-Block-Position Specific Scoring Matrix (BB-PSSM), respectively. Third, we use compositional, word embedding, and evolutionary features to generate multi-perspective fused features (MPFF). Fourth, the Multiscale Bidirectional Temporal Convolutional Network (MBiTCN) is used to train the model to process features at multiscale and analyze sequences in both forward and backward directions. The proposed approach (IR-MBiTCN) outperforms competing deep learning (DL) and machine learning (ML)-based models on training and testing datasets, achieving 83.50 % and 79.43 % accuracy, respectively. This study represents a pioneering use of computational methodology in IR prediction, providing a scalable, efficient alternative to experimental procedures and paving the way for advances in chronic disease therapy and drug discovery.

PMID:40319974 | DOI:10.1016/j.ijbiomac.2025.143844

Comput Methods Programs Biomed. 2025 Apr 28;267:108814. doi: 10.1016/j.cmpb.2025.108814. Online ahead of print.

ABSTRACT

OBJECTIVE: Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based image quality control system to classify slit-lamp images, improving diagnostic accuracy and efficiency, particularly in telemedicine applications.

DESIGN: Cross-sectional study.

METHODS: Our Zhejiang Eye Hospital dataset comprised 2982 slit-lamp images as the internal dataset. Two external datasets were included: 13,554 images from the Aier Guangming Eye Hospital (AGEH) and 9853 images from the First People's Hospital of Aksu District in Xinjiang (FPH of Aksu). We developed a Hybrid Prior-Net (HP-Net), a novel network that combines a ResNet-based classification branch with a prior knowledge branch leveraging Hough circle transform and frequency domain blur detection. The two branches' features are channel-wise concatenated at the fully connected layer, enhancing representational power and improving the network's ability to classify eligible, misaligned, blurred, and underexposed corneal images. Model performance was evaluated using metrics such as accuracy, precision, recall, specificity, and F1-score, and compared against the performance of other deep learning models.

RESULTS: The HP-Net outperformed all other models, achieving an accuracy of 99.03 %, precision of 98.21 %, recall of 95.18 %, specificity of 99.36 %, and an F1-score of 96.54 % in image classification. The results demonstrated that HP-Net was also highly effective in filtering slit-lamp images from the other two datasets, AGEH and FPH of Aksu with accuracies of 97.23 % and 96.97 %, respectively. These results underscore the superior feature extraction and classification capabilities of HP-Net across all evaluated metrics.

CONCLUSIONS: Our AI-based image quality control system offers a robust and efficient solution for classifying corneal images, with significant implications for telemedicine applications. By incorporating slightly blurred but diagnostically usable images into training datasets, the system enhances the reliability and adaptability of AI tools for medical imaging quality control, paving the way for more accurate and efficient diagnostic workflows.

PMID:40319841 | DOI:10.1016/j.cmpb.2025.108814

Sci Rep. 2025 May 4;15(1):15612. doi: 10.1038/s41598-025-99628-y.

ABSTRACT

Dengue is an important tropical disease with considerable global impact. Despite this, there remains an urgent need for reliable biomarkers to predict disease severity, as well as effective antiviral drugs and targeted treatments. In this study, we conducted a comprehensive profiling of 41 plasma mediators in patients with asymptomatic dengue (AD) and symptomatic dengue (SD), which includes mild dengue fever (DF) and severe dengue hemorrhagic fever (DHF). Our findings revealed that the levels of nearly all measured mediators were consistently lower in AD compared to SD patients, suggesting a potential protective cytokine response signature. Time-course cytokine analysis in SD shown significantly elevated levels of pro-inflammatory cytokines and chemokines associated with inflammation and viral clearance upon the acute phase, while various growth factors were elevated during the convalescence. Notably, we identified elevated IL-15 levels in DHF patients three days before fever subsidence, highlighting its potential as an early prognostic biomarker for severe disease outcomes. Furthermore, prolonged high levels of IL-8 and IP-10 in DHF during the critical period may contribute to dengue immunopathogenesis. This study advances the understanding of cytokine dynamics in the natural course of human dengue infection, providing valuable insights for the development of targeted treatments and prognostic biomarkers.

PMID:40320430 | DOI:10.1038/s41598-025-99628-y

Acta Biomater. 2025 May 2:S1742-7061(25)00328-9. doi: 10.1016/j.actbio.2025.05.007. Online ahead of print.

ABSTRACT

Scaffold-guided bone regeneration (SGBR) offers a promising solution for treating large-volume bone defects. However, its efficacy in compromised healing environments, such as those associated with metabolic conditions like Type 2 Diabetes (T2D), remains poorly understood. This study evaluates the potential of 3D-printed polycaprolactone (PCL) scaffolds for large-volume bone regeneration in preclinical models simulating T2D-induced metabolic challenges. Our results reveal that scaffolds alone are insufficient to overcome the metabolic barriers to effective bone regeneration. Metabolomic analysis of regenerating tissue identified significant disruptions in key metabolic pathways involved in energy production and amino acid synthesis in T2D rats compared to controls. Notably, aconitic acid, ornithine, and glycine levels were elevated in non-diabetic conditions, whereas phosphoenolpyruvate was markedly increased under T2D conditions. Secondary harmonic generation (SHG) imaging further demonstrated impaired collagen organization within T2D regenerating tissue, correlating with disrupted collagen synthesis critical for bone matrix formation. In vitro, the exogenous supplementation of alpha-ketoglutarate (α-KG)-a crucial citric acid cycle intermediate-enhanced mineralized tissue formation in human adipose-derived mesenchymal stem cells (hAdMSCs) from T2D donors, achieving levels superior to non-T2D cells. These findings underscore the metabolic underpinnings of impaired bone regeneration in T2D. Optimized 3D printed scaffolds alone do not counterbalance the impaired regeneration in T2D. Here we highlight a therapeutic potential of metabolic supplementation to optimize SGBR outcomes. This study provides a critical foundation for advancing translational research and developing regenerative therapies tailored to high-risk metabolic disease populations. STATEMENT OF SIGNIFICANCE: Scaffold-guided bone regeneration (SGBR) holds great promise for addressing large bone defects, but its efficacy in metabolically challenged conditions like Type 2 Diabetes (T2D) remains limited. This study uses a metabolomics-driven approach to reveal how metabolic dysregulation in T2D, including disruptions in energy and amino acid pathways, impairs collagen organization and extracellular matrix (ECM) formation-critical for successful bone healing. By identifying α-ketoglutarate (α-KG) as a potential supplement to restore metabolic balance, this work offers novel insights into enhancing scaffold performance under compromised conditions. These findings provide a foundation for integrating bioactive compounds into scaffold designs, advancing personalized strategies in regenerative medicine, and addressing a critical gap in bone defect treatment for diabetic patients.

PMID:40319991 | DOI:10.1016/j.actbio.2025.05.007

Comput Biol Med. 2025 May 3;192(Pt A):110284. doi: 10.1016/j.compbiomed.2025.110284. Online ahead of print.

ABSTRACT

BACKGROUND: The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.

METHOD: This study presents a novel survival analysis pipeline applied to multiomics data, including transcriptome, methylation, and proteome data, extracted from endometrioid samples in the TCGA-UCEC project to identify potential survival biomarkers. A major innovation in our work was the development of a deep learning autoencoder designed to capture the complex non-linear relationships between biological variables and survival outcomes. To achieve this, we defined a new loss function specifically for the autoencoder.

RESULT: The newly defined loss function can lead to extracting more survival information. The output of our pipeline includes 346 features ranked by their survival importance based on SHAP analysis, with a focus on the top 30 features. We analyzed the biological pathways enriched by these omics data and their contributions. As a result, we identified a relationship between Vitamin D, its receptor, and the Galanin receptor pathways with survival in endometrioid cancer.

CONCLUSION: This study introduces an innovative approach to survival analysis using multi-omics data and deep learning, with a greater focus on censored data to extract more survival information. It offers potential biomarkers for improved prognostic evaluation in endometrial cancer and presents pathway associations related to survival. These findings contribute to a better understanding of the progression of endometrial cancer.

PMID:40319755 | DOI:10.1016/j.compbiomed.2025.110284