Angew Chem Int Ed Engl. 2025 May 3:e202425206. doi: 10.1002/anie.202425206. Online ahead of print.

ABSTRACT

New compounds targeting human malaria parasites are critical for effective malaria control and elimination. Here, we pursued the imidazoquinolinone AZD0156 (MMV1580483), a human ataxia-telangiectasia mutated (ATM) kinase inhibitor that completed Phase I clinical trials as an anticancer agent. We validated its in vitro activity against the two main forms of the Plasmodium falciparum parasite in the human host, viz. the asexual blood (symptomatic) stage and sexual gametocyte (transmission) stage. Resistance selection, cross-resistance, biochemical and conditional knockdown studies revealed that AZD0156 inhibits P. falciparum phosphatidylinositol 4-kinase type III beta (PfPI4Kβ), a clinically-validated target for the treatment of malaria. Metabolic perturbations, fixed-ratio isobolograms, killing kinetics and morphological evaluation correlated AZD0156 inhibition with other known PI4Kβ inhibitors. The compound showed favourable in vivo pharmacokinetic properties and 81% antimalarial efficacy (4 x 50 mg/kg) in a P. berghei mouse malaria infection model. Importantly, a cleaner biochemical profile was measured against human kinases (MAP4K4, MINK1) implicated in embryofoetal developmental toxicity associated with the PfPI4Kβ inhibitor MMV390048. This improved kinase selectivity profile and structural differentiation from other PI4Kβ inhibitors, together with its multistage antiplasmodial activity and favourable pharmacokinetic properties, makes AZD0156 an attractive candidate for target-based drug repositioning against malaria via a medicinal chemistry optimisation approach.

PMID:40317875 | DOI:10.1002/anie.202425206

Pharmacol Res Perspect. 2025 Jun;13(3):e70103. doi: 10.1002/prp2.70103.

NO ABSTRACT

PMID:40317878 | DOI:10.1002/prp2.70103

Adv Sci (Weinh). 2025 May 3:e2501293. doi: 10.1002/advs.202501293. Online ahead of print.

ABSTRACT

AI-assisted electronic nose systems often emphasize sensitivity-driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving classification accuracy through deep learning, neglecting the fundamental role of sensor material design. This study addresses these challenges by developing a highly reliable sensor platform to standardize gas sensing for deep learning applications. Specifically, 1D SnO2 nanonetworks functionalized with Au and Pd nanocatalysts are fabricated via a systematic deposition process, enhancing gas diffusion and reaction kinetics. Stability improvements through controlled aging process reduce the coefficient of variation to below 5% across seven target gases: acetone, hydrogen, ethanol, carbon monoxide, propane, isoprene, and toluene. The platform exhibits exceptional deep learning performance, achieving over 99.5% classification accuracy using a residual network model, even in high-humidity environments (up to 80% relative humidity) and at parts-per-trillion detection limits. This study highlights the synergy between nanostructure engineering and AI, establishing a robust framework for next-generation bioinspired electronic nose systems with enhanced reliability and analytical capability.

PMID:40318170 | DOI:10.1002/advs.202501293

Med Phys. 2025 May 3. doi: 10.1002/mp.17865. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter-reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case.

PURPOSE: This study aims to develop a deep learning-based technique to generate multiple CTV contours for each case, simulating the inter-reader variability in the clinical practice.

METHODS: We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG-PET), x-ray computed tomography (CT), and pre-contrast T1-weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi-modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model-based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter-reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation.

RESULTS: The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state-of-the-art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state-of-the-art models). It also achieved the second-highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation.

CONCLUSIONS: The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter-reader variability in CTV delineation.

PMID:40317577 | DOI:10.1002/mp.17865

Med Phys. 2025 May 3. doi: 10.1002/mp.17862. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resection, leading to an increased risk of death. Cancer staging is currently the gold standard for establishing a patient's prognosis and can help clinicians determine patients who may benefit from additional therapy. However, medical images which are used to help determine the cancer stage, have been shown to hold unutilized prognostic information that can augment clinical data and better identify high-risk NSCLC patients. There remains an unmet need for models to incorporate clinical, pathological, surgical, and imaging information, and extend beyond the current staging system to assist clinicians in identifying patients who could benefit from additional therapy immediately after surgery.

PURPOSE: We aimed to determine whether a deep learning model (DLM) integrating FDG PET and CT imaging from the thoracic cavity along with clinical, surgical, and pathological information can predict NSCLC recurrence-free survival (RFS) and stratify patients into risk groups better than conventional staging.

MATERIALS AND METHODS: Surgically resected NSCLC patients enrolled between 2009 and 2018 were retrospectively analyzed from two academic institutions (local institution: 305 patients; external validation: 195 patients). The thoracic cavity (including the lungs, mediastinum, pleural interfaces, and thoracic vertebrae) was delineated on the preoperative FDG PET and CT images and combined with each patient's clinical, surgical, and pathological information. Using the local cohort of patients, a multi-modal DLM using these features was built in a training cohort (n = 225), tuned on a validation cohort (n = 45), and evaluated on testing (n = 35) and external validation (n = 195) cohorts to predict RFS and stratify patients into risk groups. The area under the curve (AUC), Kaplan-Meier curves, and log-rank test were used to assess the prognostic value of the model. The DLM's stratification performance was compared to the conventional staging stratification.

RESULTS: The multi-modal DLM incorporating imaging, pathological, surgical, and clinical data predicted RFS in the testing cohort (AUC = 0.78 [95% CI:0.63-0.94]) and external validation cohort (AUC = 0.66 [95% CI:0.58-0.73]). The DLM significantly stratified patients into high, medium, and low-risk groups of RFS in both the testing and external validation cohorts (multivariable log-rank p < 0.001) and outperformed conventional staging. Conventional staging was unable to stratify patients into three distinct risk groups of RFS (testing: p = 0.94; external validation: p = 0.38). Lastly, the DLM displayed the ability to further stratify patients significantly into sub-risk groups within each stage in the testing (stage I: p = 0.02, stage II: p = 0.03) and external validation (stage I: p = 0.05, stage II: p = 0.03) cohorts.

CONCLUSION: This is the first study to use multi-modality imaging along with clinical, surgical, and pathological data to predict RFS of NSCLC patients after surgery. The multi-modal DLM better stratified patients into risk groups of poor outcomes when compared to conventional staging and further stratified patients within each staging classification. This model has the potential to assist clinicians in better identifying patients that may benefit from additional therapy.

PMID:40317503 | DOI:10.1002/mp.17862

Radiol Med. 2025 May 3. doi: 10.1007/s11547-025-02013-y. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Intermediate-stage HCC is often treated with either transcatheter arterial chemoembolisation (TACE) or transcatheter arterial embolisation (TAE). Integrating machine learning (ML) offers the possibility of improving treatment outcomes through enhanced patient selection. This systematic review evaluates the effectiveness of ML models in improving the precision and efficacy of both TACE and TAE for intermediate-stage HCC. A comprehensive search of PubMed, EMBASE, Web of Science, and Cochrane Library databases was conducted for studies applying ML models to TACE and TAE in patients with intermediate-stage HCC. Seven studies involving 4,017 patients were included. All included studies were from China. Various ML models, including deep learning and radiomics, were used to predict treatment response, yielding a high predictive accuracy (AUC 0.90). However, study heterogeneity limited comparisons. While ML shows potential in predicting treatment outcomes, further research with standardised protocols and larger, multi-centre trials is needed for clinical integration.

PMID:40317437 | DOI:10.1007/s11547-025-02013-y

Int J Comput Assist Radiol Surg. 2025 May 3. doi: 10.1007/s11548-025-03356-7. Online ahead of print.

ABSTRACT

PURPOSE: Quantitative magnetic resonance imaging (qMRI) enables imaging of physical parameters related to the nuclear spin of protons in tissue, and is poised to revolutionize clinical research. However, improving the accuracy and clinical relevance of qMRI is essential for its practical implementation. This requires significantly reducing the currently lengthy acquisition times to enable clinical examinations and provide an environment where clinical accuracy and reliability can be verified. Deep learning (DL) has shown promise in significantly reducing imaging time and improving image quality in recent years. This study introduces a novel approach, quantitative deep cascade of convolutional network (qDC-CNN), as a framework for accelerated quantitative parameter mapping, offering a potential solution to this challenge. This work aims to verify that the proposed model outperforms the competing methods.

METHODS: The proposed qDC-CNN is an integrated deep-learning framework combining an unrolled image reconstruction network and a fully connected neural network for parameter estimation. Training and testing utilized simulated multi-slice multi-echo (MSME) datasets generated from the BrainWeb database. The reconstruction error with ground truth was evaluated using normalized root mean squared error (NRMSE) and compared with conventional DL-based methods. Two validation experiments were performed: (Experiment 1) assessment of acceleration factor (AF) dependency (AF = 5, 10, 20) with fixed 16 echoes, and (Experiment 2) evaluation of the impact of reducing contrast images (16, 8, 4 images).

RESULTS: In most cases, the NRMSE values of S0 and T2 estimated from the proposed qDC-CNN were within 10%. In particular, the NRMSE values of T2 were much smaller than those of the conventional methods.

CONCLUSIONS: The proposed model had significantly smaller reconstruction errors than the conventional models. The proposed method can be applied to other qMRI sequences and has the flexibility to replace the image reconstruction module to improve performance.

PMID:40317423 | DOI:10.1007/s11548-025-03356-7

ACS Nano. 2025 May 3. doi: 10.1021/acsnano.4c15032. Online ahead of print.

ABSTRACT

The search for and design of superconductors with both Kagome lattice and hardness is a challenging and frontier research topic. This work utilizes structure predictions to discover the Kagome lattice in NaSi3_P6/mmm phase of NaxSiy (x, y = 1-3). For a comprehensive understanding of XY3_P6/mmm, other atoms such as X = Li, Na, Cs and Y = B, Si, Ge are included. Superconducting critical temperatures (Tc) of XY3 compounds are calculated between 0 and 20 GPa and found to be 30.54 K for CsB3 at 0 GPa, indicating that electron-phonon coupling, phonon softening, linewidths, and electron density at the Fermi level all have significant effects on Tc. The bonding type of B, Si, and Ge atoms in the Kagome lattice also determines the boundaries of its hard properties and superconductivity. Moreover, the melting temperature of NaSi3_P6/mmm is determined to be 608 K at 0 GPa and P-T phase diagram at pressures of 0-15 GPa using deep learning molecular dynamics simulations. Our findings provide a multitude of excellent properties in the XY3 compounds, including Kagome lattice, high hardness, and superconductors, which will provide essential physical insights and theoretical guidance for the experimental exploration of the hard superconductors.

PMID:40317254 | DOI:10.1021/acsnano.4c15032

J Cancer Res Ther. 2025 May 1;21(2):362-370. doi: 10.4103/jcrt.jcrt_1910_24. Epub 2025 May 2.

ABSTRACT

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).

METHODS: This multicenter retrospective study comprised 185 CRLM patients who were categorized into three cohorts: training (n = 88), internal test (n = 39), and external test (n = 58). A total of 1126 radiomic features and 2589 DL signatures were extracted from each region of interest in the CECT. Fourteen significant radiomic features associated with RAS mutation were selected. Subsequently, various models (DL-arterial phase (AP), DL-venous phase (VP), AP+VP-DL, radiomics, and DL-R) were developed and validated. The model performance was compared using the area under the receiver operating characteristic (AUROC) curves and the DeLong test. The predictive usefulness of the DL score for progression-free survival and overall survival (OS) was determined.

RESULTS: The AP+VP-DL model achieved the highest AUC (0.98), outperforming the radiomics (0.90), DL-AP (0.93), DL-VP (0.87), and DL-R (0.97) models. Significant associations were observed between OS and the carcinoembryonic antigen (CEA), disease control rate (DCR), and DL scores, leading to the development of a DL nomogram. A high-risk RAS mutation status correlated with significantly lower 1-year (88% vs. 96%), 3-year (12% vs. 35%), and 5-year (0% vs. 15%) cumulative survival rates compared to a low-risk status (P = 0.03).

CONCLUSIONS: The DL model demonstrated satisfactory predictive performance, aiding clinicians in noninvasively predicting the RAS gene status for informed treatment decisions.

PMID:40317140 | DOI:10.4103/jcrt.jcrt_1910_24

Front Med. 2025 May 3. doi: 10.1007/s11684-025-1132-8. Online ahead of print.

ABSTRACT

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.

PMID:40317453 | DOI:10.1007/s11684-025-1132-8

Cancer Immunol Immunother. 2025 May 3;74(6):190. doi: 10.1007/s00262-025-04033-z.

ABSTRACT

Immune checkpoint inhibitors (ICI) targeting CTLA-4 and PD-1 have shown remarkable antitumor efficacy, but can also cause immune-related adverse events, including checkpoint inhibitor-induced liver injury (ChILI). This multi-omic study aimed to investigate changes in blood samples from treated cancer patients who developed ChILI. PBMCs were sequenced for by transcriptomic and T cell receptor repertoire (bulk and single-cell immune profiling), and extracellular vesicle (EV) enrichment from plasma was analyzed by mass spectroscopy proteomics. Data were analyzed by comparing the ChILI patient group to the control group who did not develop ChILI and by comparing the onset of ChILI to pre-ICI treatment baseline. We identified significant changes in T cell clonality, gene expression, and proteins in peripheral blood mononuclear cells (PBMCs) and plasma in response to liver injury. Onset of ChILI was accompanied by an increase in T cell clonality. Pathway analysis highlighted the involvement of innate and cellular immune responses, mitosis, pyroptosis, and oxidative stress. Single-cell RNA sequencing revealed that these changes were primarily found in select T cell subtypes (including CD8 + effector memory cells), while CD16 + monocytes exhibited enrichment in metabolic pathways. Proteomic analysis of plasma extracellular vesicles showed enrichment in liver-associated proteins among differentially expressed proteins. Interestingly, an increase in PBMC PD-L1 gene expression and plasma PD-L1 protein was also found to be associated with ChILI onset. These findings provide valuable insights into the immune and molecular mechanisms underlying ChILI as well as potential biomarkers of ChILI.Trial registration number NCT04476563.

PMID:40317333 | DOI:10.1007/s00262-025-04033-z

medRxiv [Preprint]. 2025 Apr 23:2025.04.22.25326170. doi: 10.1101/2025.04.22.25326170.

ABSTRACT

Colorectal cancer (CRC) remains a major health concern, with over 150,000 new diagnoses and more than 50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (e.g., Visium, Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented Virtual RNA Inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST dataset to date, comprising 45 patients and more than 300,000 Visium spots from primary tumors. Using state-of-the-art architectures (UNI, ResNet-50, ViT, and VMamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

PMID:40313260 | PMC:PMC12045403 | DOI:10.1101/2025.04.22.25326170

BMC Immunol. 2025 May 2;26(1):35. doi: 10.1186/s12865-025-00714-7.

ABSTRACT

BACKGROUND: This study utilizes the FDA Adverse Event Reporting System (FAERS) database to compare the adverse reaction signals of cyclosporine and tacrolimus, two widely used immunosuppressants, in relation to drug-induced kidney injury. The findings aim to inform clinical decision-making.

METHODS: The study retrospectively analyzed data from January 2004 to September 2024, employing both frequency analysis and Bayesian methods. We assessed and compared the mortality rates, hospitalization rates, and the association of cyclosporine and tacrolimus with kidney injury to elucidate the renal toxicity of these two drugs.

RESULTS: After data processing, we identified a total of 3,449 cyclosporine-related kidney injury reports and 5,538 tacrolimus-related kidney injury reports. The results revealed a stronger association between tacrolimus and kidney injury. Additionally, kidney injuries associated with both cyclosporine and tacrolimus predominantly affected males. Furthermore, the hospitalization rate for cyclosporine-related kidney injury was 34.40%, compared to 44.50% for tacrolimus. The mortality rate associated with cyclosporine-induced kidney injury was higher than that of tacrolimus.

CONCLUSION: This study utilized the FDA Adverse Event Reporting System (FAERS) database from January 2004 to September 2024 to perform a comprehensive analysis of adverse drug-related kidney injury reactions to cyclosporine and tacrolimus. The results suggest that both cyclosporine and tacrolimus are associated with renal injury, but tacrolimus appears to reduce mortality while increasing hospitalization rates. This serves as a critical warning for planning future treatment regimens, drug monitoring, and reducing adverse effects.

PMID:40316906 | DOI:10.1186/s12865-025-00714-7

Ultrasound Med Biol. 2025 May 1:S0301-5629(25)00094-8. doi: 10.1016/j.ultrasmedbio.2025.03.015. Online ahead of print.

ABSTRACT

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the need for advancements in diagnostic methodologies to improve early detection and treatment outcomes. This systematic review examines the integration of advanced deep learning (DL) techniques in echocardiography for detecting cardiovascular abnormalities, adhering to PRISMA 2020 guidelines. Through a comprehensive search across databases like IEEE Xplore, PubMed, and Web of Science, 29 studies were identified and analyzed, focusing on deep convolutional neural networks (DCNNs) and their role in enhancing the diagnostic precision of echocardiographic assessments. The findings highlight DL's capability to improve the accuracy and reproducibility of detecting and classifying echocardiographic data, particularly in measuring left ventricular function and identifying wall motion abnormalities. Despite these advancements, challenges such as data diversity, image quality, and the computational demands of DL models hinder their broader clinical adoption. In conclusion, DL offers significant potential to enhance the diagnostic capabilities of echocardiography. However, successful clinical implementation requires addressing issues related to data quality, computational demands, and system integration.

PMID:40316488 | DOI:10.1016/j.ultrasmedbio.2025.03.015

BMJ Open. 2025 May 2;15(5):e094764. doi: 10.1136/bmjopen-2024-094764.

ABSTRACT

INTRODUCTION: Updated primary prevention strategies are needed for post-infarction sudden cardiac death (SCD) based on implantable cardioverter-defibrillator (ICD). Current recommendations, based on left ventricular systolic function and functional class, may be obsolete because they are derived from ancient studies that do not incorporate the potential benefit of either current comprehensive treatment of ischaemic heart disease or modern device programming. Among patients with post-infarction left ventricular dysfunction, modern implantable cardiac monitoring devices (ICM) allow a unique opportunity to determine in real-time the burden of non-sustained ventricular tachycardias and their relationship to the subsequent occurrence of sustained or symptomatic events.

METHODS AND ANALYSIS: Approximately 200 patients with left ventricular ejection fraction (LVEF) equal to or less than 40% after acute myocardial infarction will be included in the study. They will be implanted with a Confirm RX, an ICM with real-time remote connection via a smartphone. At 6 months, LVEF and functional status will be re-evaluated and cardiac morpho-functional characterisation will be performed by MRI. At this time, and following current European guidelines, patients with an indication will receive an ICD; the others will continue to be monitored using an ICM for a minimum of 2 years. Patients are expected to be followed up for 4 years after the index event. More than 20 000 remote transmissions are expected to be analysed. The study will focus on the relationship between the detection of non-sustained ventricular tachycardias by ICMs (defined as at least 8 R-R intervals at 160 beats per minute) and the subsequent occurrence of symptomatic arrhythmic events. An advanced statistical analysis will be performed using machine and deep learning techniques to determine the clinical variables, those that are derived from monitoring and imaging tests and related to mid-term prognosis.

ETHICS AND DISSEMINATION: The study was approved by the Ethical Committee of the University Hospital of Salamanca (protocol number PI 2019 03 246) on 30 April 2020. Each patient will be informed about the study in both oral and written form by a physician and will be included in the study after written consent is obtained.For the first time, a study will provide real-time information on the arrhythmic burden of patients with post-infarction ventricular dysfunction and its prognostic implications in the medium term. Several publications in scientific journals are planned.

TRIAL REGISTRATION NUMBER: NCT04765943.

PMID:40316360 | DOI:10.1136/bmjopen-2024-094764

Neuroimage. 2025 Apr 30:121246. doi: 10.1016/j.neuroimage.2025.121246. Online ahead of print.

ABSTRACT

OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tracer compartment modeling and machine learning, provide promising solutions. This study explores the deep learning (DL)-based single-session triple-tracer brain PET imaging protocol, aiming at simplifying multi-tracer PET imaging, while reducing radiation exposure.

METHODS: The study uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes cognitively normal (CN) patients, as well as patients with mild cognitive impairment (MCI) and dementia. The dataset also includes PET scans acquired with amyloid (18F-florbetaben [FBB] or 18F-florbetapir [FBP]), 18F-Fluorodeoxyglucose (FDG), and tau 18F-flortaucipir (FTP). To mimic the effect of simultaneous acquisition of multiple PET tracers, we generated synthetic dual- and triple-tracer images by summing FBP/FBB, FTP, and FDG scans. A DL model based on Swin Transformer architecture was developed to separate these signals, using five-fold cross-validation and mean squared error (MSE) loss. The synthetic PET images were evaluated using established image quality metrics, including MSE, structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). In addition, clinical evaluation was conducted by two nuclear medicine specialists to assess the amyloid and tau status in the synthetic and reference images.

RESULTS: The proposed DL model effectively synthesized realistic FBB/FBP and FDG images from dual- and triple-tracer PET images. Although the proposed DL model's performance in generating FTP images was less successful, it remains promising. The clinical evaluation revealed that the amyloid status estimated from the synthetic images led to a sensitivity of 92% and specificity of 86% for FBB, while it showed a sensitivity of 93% and specificity of 67% for tau status using FBP extracted from the triple-tracer images. The calculated quantitative metrics showed that the mean error for synthetic amyloid images (FBB: 0.03 SUV, FBP: 0.00 SUV) was higher than FDG for FBB (0.02 SUV) but lower than FDG for FBP (-0.01 SUV), and comparable to FTP (FBB: 0.03 SUV, FBP: 0.00 SUV). Voxel-wise correlation analysis demonstrated strong correlation between synthetic and reference images, particularly for amyloid images (FBB: y = 0.98x + 0.00, R² = 0.85; FBP: y = 1.11x + 0.04, R² = 0.73), while FTP (FBB: y = 0.87x + 0.14, R² = 0.51; FBP: y = 0.98x + 0.09, R² = 0.59) and FDG images (FBB: y = 1.01x + 0.18, R² = 0.85; FBP: y = 0.96x + 1.37, R² = 0.77) showed moderate correlations.

CONCLUSION: Our study demonstrates that the suggested DL model can separate the signals belonging to three different radiotracers from simultaneous triple-tracer PET scans. This method may make multiplex scanning feasible in the clinic, hence reducing the scanning time, radiation hazard and improving patient comfort.

PMID:40316225 | DOI:10.1016/j.neuroimage.2025.121246

J Neurosci Methods. 2025 Apr 30:110463. doi: 10.1016/j.jneumeth.2025.110463. Online ahead of print.

ABSTRACT

BACKGROUND: Taste perception is the process by which the gustatory system detects and interprets chemical stimuli from food and beverages, involving activation of taste receptors on the tongue. Analyzing taste perception is essential for understanding human sensory responses and diagnosing taste-related disorders.

NEW METHOD: This research focuses on developing a deep learning framework to effectively recognize basic taste stimuli from EEG signals. Initially, the recorded EEG signals undergo preprocessing to remove noise and artifacts. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) method is then applied to decompose the EEG signals into various frequency rhythms, referred to as intrinsic mode functions (IMFs). From the chosen IMFs, six distinct entropy features-sample, bubble, approximate, dispersion, slope, and permutation entropy-are extracted for further analysis. A novel deep learning model, TasteNet, is then developed, integrating a convolutional neural network (CNN) module, a multi-head attention module, and the Att-BiPLSTM (Attention-Bidirectional Potent Long Short-Term Memory) network.

RESULTS: The proposed architecture classifies the input data into six categories: no taste, sweet, sour, bitter, umami, and salty, achieving a remarkable accuracy of 97.52 ±0.48%.

COMPARISON WITH EXISTING METHODS: TasteNet outperforms existing taste perception classification methods, as demonstrated through extensive experiments.

CONCLUSION: This study presents TasteNet, a robust framework for precise taste perception recognition using EEG signals. Using CEEMDAN for effective signal decomposition and extracting key entropy features, the model captures intricate patterns in taste stimuli. The incorporation of multi-head attention module and the Att-BiPLSTM network further enhances the model's ability to identify various taste sensations accurately.

PMID:40315923 | DOI:10.1016/j.jneumeth.2025.110463

Comput Biol Med. 2025 May 1;192(Pt A):110234. doi: 10.1016/j.compbiomed.2025.110234. Online ahead of print.

ABSTRACT

The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (electrocardiogram) stands out given its critical role in healthcare, particularly for the diagnosis and management of cardiovascular diseases. Numerous studies have demonstrated that ECG contains traits to distinctively identify a person. As a result, the need for anonymization methods is becoming increasingly crucial to protect personal privacy while ensuring the integrity of health data for effective clinical utility. Although many anonymization methods have been proposed in the literature, there has been limited exploration into their ability to preserve data integrity while complying with stringent data protection regulations. More specifically, the utility of anonymized signal and the privacy level achieved often present a trade-off that has not been thoroughly addressed. This paper analyzes the trade-off between balancing privacy protection with the preservation of health data integrity in ECG signals focusing on memory-efficient anonymization techniques that are suitable for real-time or streaming applications and do not require heavy memory computation. Moreover, we introduce an analytical framework to evaluate the privacy preservation methods alongside health integrity, incorporating state-of-the-art disease and person identifiers. We also propose a novel metric that assists users in selecting an anonymization method based on their desired trade-off between health insights and privacy protection. The experimental results demonstrate the impact of the de-identification techniques on critical downstream tasks, such as Arrhythmia detection and Myocardial Infarction detection along with identification performance, while statistical analysis reveals the biometric nature of ECG signals. The findings highlight the limitations of using such anonymization methods and models, emphasizing the need for approaches that maintain the clinical relevance of ECG data in real-time and streaming applications, particularly in memory-constrained environments.

PMID:40315720 | DOI:10.1016/j.compbiomed.2025.110234

Comput Biol Med. 2025 May 1;192(Pt A):110273. doi: 10.1016/j.compbiomed.2025.110273. Online ahead of print.

ABSTRACT

Plankton are microorganisms that play an important role in marine food webs as primary producers in the trophic web. Traditional plankton identification methods using manual microscopy and sampling are time-consuming, labor-intensive, and prone to errors. Deep learning has improved the automation of plankton identification, but it remains challenging to achieve high accuracy and efficiency in computation with limited labeled data. In this paper, we proposed an improved plankton classification model that is more accurate and interpretable. We train two models, InceptionResNetV2 (transfer learning) and DeepPlanktonNet (from scratch), on the WHOI dataset. We utilize feature fusion to supplement feature representation, merging the outputs of both models. Feature selection is achieved through the Whale Optimization Algorithm (WOA), eliminating redundancy and making it more computationally efficient. Additionally, we also employ Local Interpretable Model-agnostic Explanations (LIME) to make the model more interpretable and gain insights into how the model makes decisions. Additionally, feature selection using WOA reduces feature space and has less inference and computational cost. Our method achieves a classification accuracy of 98.79 %, which is better than previous state-of-the-art methods. For robustness testing, we train nine machine learning classifiers on the optimized features. By significantly improving classification accuracy and speed, our method enables large-scale ecological surveys, water quality monitoring, and biodiversity studies. These advances allow researchers to and environmental scientists to automate plankton classification more reliably, supporting marine conservation and resource management.

PMID:40315719 | DOI:10.1016/j.compbiomed.2025.110273

Antiviral Res. 2025 Apr 30:106184. doi: 10.1016/j.antiviral.2025.106184. Online ahead of print.

ABSTRACT

Helicases have emerged as promising targets in antiviral drug development but remain largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all helicases annotated in the ChEMBL database. After thoroughly curating and enriching the data with accurate annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). Heli-SMACC contains 13,597 molecules, 29 proteins, and 20,431 bioactivity entries for viral, human, and bacterial helicases. We selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. While Heli-SMACC provides a rich resource for identifying candidate inhibitors, cross-species compound transferability remains a significant challenge. In particular, inhibitory activity observed against viral helicases often does not translate well to human or bacterial homologs and vice versa due to differences in binding site composition, helicase structure, and cofactor dependencies. Despite these limitations, Heli-SMACC offers a valuable starting point for structure-based optimization and target-specific inhibitor design. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu.

PMID:40316178 | DOI:10.1016/j.antiviral.2025.106184