Biophys Chem. 2025 Mar 10;322:107434. doi: 10.1016/j.bpc.2025.107434. Online ahead of print.

ABSTRACT

Emergence of viruses cause unprecedented challenges and thus leading to wide-ranging consequences today. The world has faced massive disruptions like COVID-19 and continues to suffer in terms of public health and world economy. Fighting with this emergence of viruses and its reemergence plays a critical role in the health care industry. Identification of novel virus-drug associations is a vital step in drug discovery. Prediction and prioritization of novel virus-drug associations through computational approaches is an alternative and best choice considering the cost and risk of biological experiments. This study proposes a method, KR-AEVDA that relies on k-nearest neighbor based reliable negative sample selection and autoencoder based feature extraction to explore promising virus-drug associations for further experimental validation. The method analyzes complex relationships among drugs and viruses by investigating similarity and association data between drugs and viruses. It generates feature vectors from the similarity data, and reliable negative samples are extracted through an effective distance-based algorithm from the unlabeled samples in the dataset. Then high level features are extracted via an autoencoder and is fed to an ensemble classifier for inferring novel associations. Experimental results on three different datasets showed that KR-AEVDA reliably attained better performance than other state-of-the-art methods. Molecular docking is carried out between the top-predicted drugs and the crystal structure of the SARS-CoV-2's main protease to further validate the predictions. Case studies for SARS-CoV-2 illustrate the effectiveness of KR-AEVDA in identifying potential virus-drug associations.

PMID:40096790 | DOI:10.1016/j.bpc.2025.107434

Pancreatology. 2025 Feb 28:S1424-3903(25)00043-2. doi: 10.1016/j.pan.2025.02.015. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic exocrine insufficiency (PEI) is defined as a reduction in pancreatic exocrine secretion below a level that allows normal digestion of nutrients. Pancreatic disease and pancreatic surgery are the main causes of PEI, but other conditions can affect the digestive function of the pancreas.

METHODS: In collaboration with European Digestive Surgery (EDS), European Society for Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN), European Society for Clinical Nutrition and Metabolism (ESPEN), European Society of Digestive Oncology (ESDO), and European Society of Primary Care Gastroenterology (ESPCG) the working group developed European guidelines for the diagnosis and therapy of PEI. United European Gastroenterology (UEG) provided both endorsement and financial support for the development of the guidelines.

RESULTS: Recommendations covered topics related to the clinical management of PEI: concept, pathogenesis, clinical relevance, general diagnostic approach, general therapeutic approach, PEI secondary to chronic pancreatitis, PEI after acute pancreatitis, PEI associated with pancreatic cancer, PEI secondary to cystic fibrosis, PEI after pancreatic surgery, PEI after esophageal, gastric, and bariatric surgery, PEI in patients with type 1 and type 2 diabetes, and PEI in other conditions.

CONCLUSIONS: The European guidelines for the diagnosis and therapy of PEI provide evidence-based recommendations concerning key aspects of the etiology, diagnosis, therapy, and follow-up, based on current available evidence. These recommendations should serve as a reference standard for existing management of PEI and as a guide for future clinical research. This article summarizes the recommendations and statements.

PMID:40097316 | DOI:10.1016/j.pan.2025.02.015

Sci Total Environ. 2025 Mar 15;972:179010. doi: 10.1016/j.scitotenv.2025.179010. Online ahead of print.

ABSTRACT

Sewage contamination of freshwater occurs in the form of raw waste or as effluent from wastewater treatment plants (WWTP's). While raw waste (animal and human) and under-functioning WWTP's can introduce live enteric bacteria to freshwater systems, most WWTP's, even when operating correctly, do not remove bacterial genetic material from treated waste, resulting in the addition of bacterial DNA, including antibiotic resistance genes, into water columns and sediment of freshwater systems. In freshwater systems with both raw and treated waste inputs, then, there will be increased interaction between live sewage-associated bacteria (untreated sewage) and DNA contamination (from both untreated and treated wastewater effluent). To evaluate this understudied interaction between DNA and bacterial contamination in the freshwater environment, we conducted a three-month field-based study of sewage-associated bacteria and genetic material in water and sediment in a freshwater tributary of the Hudson River (NY, USA) that supplies drinking water and receives treated and untreated wastewater discharges from several municipalities. Using both DNA and culture-based bacterial analyses, we found that both treated and untreated sewage influences water and sediment bacterial communities in this tributary, and water-sediment exchanges of enteric bacteria and genetic material. Our results also indicated that the treated sewage effluent on this waterway serves as a concentrated source of intI1 (antibiotic resistance) genes, which appear to collect in the sediments below the outfall along with fecal indicator bacteria. Our work also captured the environmental impact of a large rain event that perturbed bacterial populations in sediment and water matrices, independently from the outflow. This study suggests that large precipitation events are an important cause of bacterial and DNA contamination for freshwater tributaries, with runoff from the surrounding environment being an important factor.

PMID:40096758 | DOI:10.1016/j.scitotenv.2025.179010

Bioinformatics. 2025 Mar 17:btaf113. doi: 10.1093/bioinformatics/btaf113. Online ahead of print.

ABSTRACT

SUMMARY: The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines Named Entity Recognition (NER) for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalisation to map these entities to databases like Ensembl, Experimental Factor Ontology (EFO), and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research >29.9 m distinct target-disease, 11.8 m distinct target-drug, and 8.3 m distinct disease-drug relationships).

AVAILABILITY AND IMPLEMENTATION: The results are accessible through Europe PMC's SciLite web app (https://europepmc.org/) and its annotations API (https://europepmc.org/annotationsapi), as well as via the Open Targets Platform (https://platform.opentargets.org/). The daily pipeline is available at https://github.com/ML4LitS/otar-maintenance, and the Open Targets ETL processes are available at https://github.com/opentargets.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40097274 | DOI:10.1093/bioinformatics/btaf113

Bioinformatics. 2025 Mar 17:btaf117. doi: 10.1093/bioinformatics/btaf117. Online ahead of print.

ABSTRACT

MOTIVATION: Identifying drug target interactions is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying drug target interactions necessitate computational tools for automated prediction and comprehension of drug target interactions. Despite recent advancements, current methods fail to fully leverage the hierarchical information in drug target interactions.

RESULTS: Here we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict drug target interactions, by integrating the structures of drugs and proteins via a low-level view GNN (LGNN) and a high-level view GNN (HGNN). The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protein node is further detailed in a low-level graph, where nodes represent molecules within each drug or amino acids within each protein, accompanied by their respective chemical descriptors. Two distinct low-level graph neural networks are first deployed to capture structural and chemical features specific to drugs and proteins from these low-level graphs. Subsequently, a high-level graph encoder is employed to comprehensively capture and merge interactive features pertaining to drugs and proteins from the high-level graph. The high-level encoder incorporates a structure and attribute information fusion module designed to explicitly integrate representations acquired from both a feature encoder and a graph encoder, facilitating consensus representation learning. Extensive experiments conducted on three benchmark datasets have shown that our proposed H2GnnDTI model consistently outperforms state-of-the-art deep learning methods.

AVAILABILITY AND IMPLEMENTATION: The codes are freely available at https://github.com/LiminLi-xjtu/H2GnnDTI.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40097269 | DOI:10.1093/bioinformatics/btaf117

Zhongguo Zhen Jiu. 2025 Mar 12;45(3):391-396. doi: 10.13703/j.0255-2930.20240207-0003. Epub 2024 Oct 28.

ABSTRACT

This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.

PMID:40097227 | DOI:10.13703/j.0255-2930.20240207-0003

BMJ Health Care Inform. 2025 Mar 17;32(1):e101192. doi: 10.1136/bmjhci-2024-101192.

ABSTRACT

BACKGROUND: Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive renal biopsy. Accurate evaluation of interstitial fibrosis and tubular atrophy (IFTA) is crucial for CKD management. This study aimed to leverage machine learning (ML) models to predict IFTA using a combination of ultrasonography (US) images and patient biomarkers.

METHODS: We retrospectively collected US images and biomarkers from 632 patients with CKD across three hospitals. The data were subjected to pre-processing, exclusion of sub-optimal images, and feature extraction using a dual-path convolutional neural network. Various ML models, including XGBoost, random forest and logistic regression, were trained and validated using fivefold cross-validation.

RESULTS: The dataset was divided into training and test datasets. For image-level IFTA classification, the best performance was achieved by combining US image features and patient biomarkers, with logistic regression yielding an area under the receiver operating characteristic curve (AUROC) of 99%. At the patient level, logistic regression combining US image features and biomarkers provided an AUROC of 96%. Models trained solely on US image features or biomarkers also exhibited high performance, with AUROC exceeding 80%.

CONCLUSION: Our artificial intelligence-based approach to IFTA classification demonstrated high accuracy and AUROC across various ML models. By leveraging patient biomarkers alone, this method offers a non-invasive and robust tool for early CKD assessment, demonstrating that biomarkers alone may suffice for accurate predictions without the added complexity of image-derived features.

PMID:40097202 | DOI:10.1136/bmjhci-2024-101192

Am J Vet Res. 2025 Mar 17:1-8. doi: 10.2460/ajvr.24.08.0248. Online ahead of print.

ABSTRACT

OBJECTIVE: The purpose of this research was to examine the feasibility of utilizing generative adversarial networks (GANs) to generate accurate pseudo-CT images for dogs.

METHODS: This study used head standard CT images and T1-weighted transverse with contrast 3-D fast spoiled gradient echo head MRI images from 45 nonbrachycephalic dogs that received treatment between 2014 and 2023. Two conditional GANs (CGANs), one with a U-Net generator and a PatchGAN discriminator and another with a residual neural network (ResNet) U-Net generator and ResNet discriminator were used to generate the pseudo-CT images.

RESULTS: The CGAN with a ResNet U-Net generator and ResNet discriminator had an average mean absolute error of 109.5 ± 153.7 HU, average peak signal-to-noise ratio of 21.2 ± 4.31 dB, normalized mutual information of 0.89 ± 0.05, and dice similarity coefficient of 0.91 ± 0.12. The dice similarity coefficient for the bone was 0.71 ± 0.17. Qualitative results indicated that the most common ranking was "slightly similar" for both models. The CGAN with a ResNet U-Net generator and ResNet discriminator produced more accurate pseudo-CT images than the CGAN with a U-Net generator and PatchGAN discriminator.

CONCLUSIONS: The study concludes that CGAN can generate relatively accurate pseudo-CT images but suggests exploring alternative GAN extensions.

CLINICAL RELEVANCE: Implementing generative learning into veterinary radiation therapy planning demonstrates the potential to reduce imaging costs and time.

PMID:40096825 | DOI:10.2460/ajvr.24.08.0248

Psychiatry Res Neuroimaging. 2025 Mar 14;349:111970. doi: 10.1016/j.pscychresns.2025.111970. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. This study proposes a framework for autism detection and region localization using an optimized deep learning approach with attention mechanisms. The pipeline includes MRI image collection, pre-processing (bias field correction, histogram equalization, artifact removal, and non-local mean filtering), and autism classification with a Symmetric Structured MobileNet with Attention Mechanism (SSM-AM). Enhanced by Refreshing Awareness-aided Election-Based Optimization (RA-EBO), SSM-AM achieves robust classification. Abnormality region localization utilizes a Multiscale Dilated Attention-based Adaptive U-Net (MDA-AUnet) further optimized by RA-EBO. Experimental results demonstrate that our proposed model outperforms existing methods, achieving an accuracy of 97.29%, sensitivity of 97.27%, specificity of 97.36%, and precision of 98.98%, significantly improving classification and localization performance. These results highlight the potential of our approach for early ASD diagnosis and targeted interventions. The datasets utilized for this work are publicly available at https://fcon_1000.projects.nitrc.org/indi/abide/.

PMID:40096789 | DOI:10.1016/j.pscychresns.2025.111970

Psychiatry Res Neuroimaging. 2025 Mar 13;349:111969. doi: 10.1016/j.pscychresns.2025.111969. Online ahead of print.

ABSTRACT

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted perception of reality. Magnetic resonance imaging (MRI) is an essential tool for diagnosing SZ which helps to understand the organization of the brain. Functional MRI (fMRI) is a specialized imaging technique to measure and map brain activity by detecting changes in blood flow and oxygenation. The proposed paper correlates the results using an explainable deep learning approach to identify the significant regions of SZ patients using group-level analysis for both structural MRI (sMRI) and fMRI data. The study found that the heat maps for Grad-CAM show clear visualization in the frontal lobe for the classification of SZ and CN with a 97.33% accuracy. The group difference analysis reveals that sMRI data shows intense voxel activity in the right superior frontal gyrus of the frontal lobe in SZ patients. Also, the group difference between SZ and CN during n-back tasks of fMRI data indicates significant voxel activation in the frontal cortex of the frontal lobe. These findings suggest that the frontal lobe plays a crucial role in the diagnosis of SZ, aiding clinicians in planning the treatment.

PMID:40096788 | DOI:10.1016/j.pscychresns.2025.111969

Int J Pediatr Otorhinolaryngol. 2025 Mar 13;192:112311. doi: 10.1016/j.ijporl.2025.112311. Online ahead of print.

ABSTRACT

IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.

OBJECTIVE: Tympanostomy tube checks are necessary but are also burdensome to families and impact access to care for other children seeking otolaryngologic care. Telemedicine care would be ideal, but ear exams are limited. This study aimed to assess whether an artificial intelligence (AI) algorithm trained with images from an over-the-counter digital otoscope can accurately assess tube status as in place and patent, extruded, or absent.

DESIGN: A prospective study of children aged 10 months to 10 years being seen for tympanostomy tube follow-up was carried out in three clinics from May-November 2023. A smartphone otoscope was used by non-MDs to capture images of the ear canal and tympanic membranes. Pediatric otolaryngologist exam findings (tube in place, extruded, absent) were used as a gold standard. A deep learning algorithm was trained and tested with these images. Statistical analysis was performed to determine the performance of the algorithm.

SETTING: 3 urban, pediatric otolaryngology clinics within an academic medical center.

PARTICIPANTS: Pediatric patients aged 10 months to 10 years with a past or current history of tympanostomy tubes were recruited. Patients were excluded from this study if they had a history of myringoplasty, tympanoplasty, or cholesteatoma. Main Outcome MeasureCalculated accuracy, sensitivity, and specificity for the deep learning algorithm in classifying tubal status as either in place and patent, extruded in external ear canal, or absent.

RESULTS: A heterogeneous group of 69 children yielded 296 images. Multiple types of tympanostomy tubes were included. The image capture success rate was 90.8 % in all subjects and 80 % in children with developmental delay/autism spectrum disorder. The classification accuracy was 97.1 %, sensitivity 97.1 %, and specificity 98.6 %.

CONCLUSION: A deep learning algorithm was trained with images from a representative pediatric population. It was highly accurate, sensitive, and specific. These results suggest that AI technology could be used to augment tympanostomy tube checks.

PMID:40096786 | DOI:10.1016/j.ijporl.2025.112311

Comput Biol Med. 2025 Mar 16;189:110002. doi: 10.1016/j.compbiomed.2025.110002. Online ahead of print.

ABSTRACT

Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which offers affordability but suffers from challenges related to accuracy and complexity. To address these limitations, we propose a deep learning model, Fetal Heart Sounds U-NetR (FHSU-NETR), capable of extracting both fetal and maternal heart rates directly from raw PCG signals. FHSU-NETR is designed for practical implementation in various healthcare environments, enhancing accessibility and reliability of fetal monitoring. Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. Validated with data from 20 normal subjects, including a case of fetal tachycardia arrhythmia, FHSU-NETR demonstrated exceptional performance. It accurately identified most of the fetal heartbeat locations with a low mean difference in fetal heart rate estimation (-2.55±10.25 bpm) across the entire dataset, and successfully detected the arrhythmia case. Similarly, FHSU-NETR showed a low mean difference in maternal heart rate estimation (-1.15±5.76 bpm) compared to the ground-truth maternal ECG. The model's exceptional ability to identify arrhythmia cases within the dataset underscores its potential for real-world application and generalization. By leveraging the capabilities of deep learning, our proposed model holds promise to reduce the reliance on medical experts for the interpretation of extensive PCG recordings, thereby enhancing efficiency in clinical settings.

PMID:40096767 | DOI:10.1016/j.compbiomed.2025.110002

Am J Physiol Lung Cell Mol Physiol. 2025 Mar 17. doi: 10.1152/ajplung.00258.2024. Online ahead of print.

ABSTRACT

Cigarette smoke (CS) is a leading cause of chronic obstructive pulmonary disease (COPD). Here, we investigated whether the ion channel amplifier nesolicaftor rescues CS-induced mucociliary and ion channel dysfunction. Since CS increases expression of transforming growth factor-beta1 (TGF-β1), human bronchial epithelial cells (HBEC) from healthy donors were used for TGF-β1 and COPD donors (COPD-HBEC) for CS exposure experiments. CS and TGF-β1 induce mucociliary dysfunction by increasing MUC5AC and decreasing ion channel conductance important for mucus hydration. These include cystic fibrosis transmembrane conductance regulator (CFTR) and apical large-conductance, Ca2+-activated K+ (BK) channels. Nesolicaftor rescued CFTR and BK channel dysfunction, restored ciliary beat frequency (CBF), and decreased mucus viscosity and MUC5AC expression in CS-exposed COPD-HBEC. Nesolicaftor further reversed reductions in ASL volumes, CBF, and CFTR and BK conductance, and blocked the increase in extracellular signal-regulated kinase (ERK) signaling in TGF-β1-exposed normal HBEC. Mechanistically, nesolicaftor increased, as expected, binding of PCBP1 to CFTR mRNA, but surprisingly also to LRRC26 mRNA, which encodes the gamma subunit required for BK function. Similar to nesolicaftor, the angiotensin receptor blocker (ARB) losartan rescued TGF-β1-mediated decreases in PCBP1 binding to LRRC26 mRNA. In addition, the ARB telmisartan restored PCBP1 binding to CFTR and LRRC26 mRNAs to rescue CFTR and BK function in CS-exposed COPD-HBEC. Thus, nesolicaftor and ARBs act on the same target and were therefore neither additive nor synergistic in their actions. These data demonstrate that nesolicaftor and ARBs may provide benefits in COPD by improving ion channel function important for mucus hydration.

PMID:40095970 | DOI:10.1152/ajplung.00258.2024

Pediatr Nephrol. 2025 Mar 17. doi: 10.1007/s00467-025-06715-3. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a life-shortening multisystem disease resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, causing the most devastating phenotypes in the airway and pancreas. Significant advances in treatment for CF lung disease, including the expanded use of high-efficiency modulator therapies (HEMT) such as Trikafta, have dramatically increased both quality of life and life expectancy for people with CF (PwCF). With these advances, long-term extrapulmonary manifestations are more frequently recognized. Pseudo-Barter syndrome, acute kidney injury (AKI) induced by medications or dehydration, amyloidosis, nephrolithiasis, and IgA and diabetic nephropathies have been previously reported in PwCF. Newer data suggest that chronic kidney disease (CKD) is a new morbidity in the aging CF population, affecting 19% of people over age 55. CKD carries a high risk of premature death from cardiovascular complications. Studies suggest that CFTR dysfunction increases kidneys' vulnerability to injury caused by the downstream effects of CF. Improving the mutant CFTR function by HEMT may help to tease apart the kidney responses resulting from extrinsic factors and those intrinsically related to the CFTR gene mutations. Additionally, given the novelty of HEMT approaches, the potential off-target effects of their long-term use are currently unknown. We review the evolving kidney complications in PwCF and propose the term CF-related kidney disease. We hope this review will increase awareness about the changing phenotype of kidney dysfunction in PwCF and help prevent morbidity related to this condition.

PMID:40095037 | DOI:10.1007/s00467-025-06715-3

Comput Methods Biomech Biomed Engin. 2025 Mar 17:1-18. doi: 10.1080/10255842.2025.2477801. Online ahead of print.

ABSTRACT

Electroencephalography (EEG) signals are widely employed due to their spontaneity and robustness against artifacts in emotion recognition. However, existing methods are often unable to fully integrate high-dimensional features and capture changing patterns in time series when processing EEG signals, which results in limited classification performance. This paper proposes an emotion recognition method (AEF-DL) based on autoencoder fusion features and MSC-TimesNet models. Firstly, we segment the EEG signal in five frequency bands into time windows of 0.5 s, extract power spectral density (PSD) features and differential entropy (DE) features, and implement feature fusion using the autoencoder to enhance feature representation. Based on the TimesNet model and incorporating the multi-scale convolutional kernels, this paper proposes an innovative deep learning model (MSC-TimesNet) for processing fused features. MSC-TimesNet efficiently extracts inter-period and intra-period information. To validate the performance of the proposed method, we conducted systematic experiments on the public datasets DEAP and Dreamer. In dependent experiments with subjects, the classification accuracies reached 98.97% and 95.71%, respectively; in independent experiments with subjects, the accuracies reached 97.23% and 92.95%, respectively. These results demonstrate that the proposed method exhibits significant advantages over existing methods, highlighting its effectiveness and broad applicability in emotion recognition tasks.

PMID:40096584 | DOI:10.1080/10255842.2025.2477801

Magn Reson Med. 2025 Mar 17. doi: 10.1002/mrm.30447. Online ahead of print.

ABSTRACT

PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based linewidth (LW) broadening of the direct water saturation (DS) curve of the water saturation spectrum (Z-spectrum) during and after glucose infusion (DS-DGE MRI).

METHODS: To estimate the glucose-infusion-induced LW changes (ΔLW), Bloch-McConnell simulations were performed for normoglycemia and hyperglycemia in blood, gray matter (GM), white matter (WM), CSF, and malignant tumor tissue. Whole-brain DS-DGE imaging was implemented at 3 T using dynamic Z-spectral acquisitions (1.2 s per offset frequency, 38 s per spectrum) and assessed on four brain tumor patients using infusion of 35 g of D-glucose. To assess ΔLW, a deep learning-based Lorentzian fitting approach was used on voxel-based DS spectra acquired before, during, and post-infusion. Area-under-the-curve (AUC) images, obtained from the dynamic ΔLW time curves, were compared qualitatively to perfusion-weighted imaging parametric maps.

RESULTS: In simulations, ΔLW was 1.3%, 0.30%, 0.29/0.34%, 7.5%, and 13% in arterial blood, venous blood, GM/WM, malignant tumor tissue, and CSF, respectively. In vivo, ΔLW was approximately 1% in GM/WM, 5% to 20% for different tumor types, and 40% in CSF. The resulting DS-DGE AUC maps clearly outlined lesion areas.

CONCLUSIONS: DS-DGE MRI is highly promising for assessing D-glucose uptake. Initial results in brain tumor patients show high-quality AUC maps of glucose-induced line broadening and DGE-based lesion enhancement similar and/or complementary to perfusion-weighted imaging.

PMID:40096575 | DOI:10.1002/mrm.30447

Magn Reson Med. 2025 Mar 17. doi: 10.1002/mrm.30473. Online ahead of print.

ABSTRACT

PURPOSE: Trityl OXO71-based pulse electron paramagnetic resonance imaging (EPRI) is an excellent technique to obtain partial pressure of oxygen (pO2) maps in tissues. In this study, we used deep learning techniques to denoise 3D EPR amplitude and pO2 maps.

METHODS: All experiments were performed using a 25 mT EPR imager, JIVA-25®. The MONAI implementation of four neural networks (autoencoder, Attention UNet, UNETR, and UNet) was tested, and the best model (UNet) was then enhanced with joint bilateral filters (JBF). The training dataset was comprised of 227 3D images (56 in vivo and 171 in vitro), 159 images for training, 45 for validation, and 23 for testing. UNet with 1, 2, and 3 JBF layers was tested to improve image SNR, focusing on multiscale structural similarity index measure and edge sensitivity preservation. The trained algorithm was tested using acquisitions with 15, 30, and 150 averages in vitro with a sealed deoxygenated OXO71 phantom and in vivo with fibrosarcoma tumors grown in a hind leg of C3H mice.

RESULTS: We demonstrate that UNet with 2 JBF layers (UNet+JBF2) provides the best outcome. We demonstrate that using the UNet+JBF2 model, the SNR of 15-shot amplitude maps provides higher SNR compared to 150-shot pre-filter maps, both in phantoms and in tumors, therefore, allowing 10-fold accelerated imaging. We demonstrate that the trained algorithm improves SNR in pO2 maps.

CONCLUSIONS: We demonstrate the application of deep learning techniques to EPRI denoising. Higher SNR will bring the EPRI technique one step closer to clinics.

PMID:40096518 | DOI:10.1002/mrm.30473

Sensors (Basel). 2025 Mar 6;25(5):1635. doi: 10.3390/s25051635.

ABSTRACT

Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.

PMID:40096500 | DOI:10.3390/s25051635

Sensors (Basel). 2025 Mar 5;25(5):1594. doi: 10.3390/s25051594.

ABSTRACT

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services. Additionally, some players, even after receiving services as per the Service Level Agreement (SLA), claim that they are not receiving services as promised. This poses a significant challenge for cloud service providers in accurately identifying QoE and improving actual services. In this paper, we have compared our previous proposed novel technique that utilizes a deep learning (DL) model to assess QoE through players' facial expressions during cloud gaming sessions in a virtual reality (VR) environment. The EmotionNET model technique is based on a convolutional neural network (CNN) architecture. Later, we have compared the EmotionNET technique with three other DL techniques, namely ConvoNEXT, EfficientNET, and Vision Transformer (ViT). We trained the EmotionNET, ConvoNEXT, EfficientNET, and ViT model techniques on our custom-developed dataset, achieving 98.9% training accuracy and 87.8% validation accuracy with the EmotionNET model technique. Based on the training and comparison results, it is evident that the EmotionNET model technique predicts and performs better than the other model techniques. At the end, we have compared the EmotionNET results on two network (WiFi and mobile data) datasets. Our findings indicate that facial expressions are strongly correlated with QoE.

PMID:40096493 | DOI:10.3390/s25051594

Sensors (Basel). 2025 Mar 6;25(5):1622. doi: 10.3390/s25051622.

ABSTRACT

The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth's system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting.

PMID:40096481 | DOI:10.3390/s25051622