Eur J Immunol. 2025 Feb;55(2):e202451225. doi: 10.1002/eji.202451225.

ABSTRACT

Regulatory T cells (Tregs) are a subset of T cells defined by the expression of Forkhead box protein P3 (FoxP3) playing a crucial role in regulating effector T cell activity. Tregs accumulate in the tumor microenvironment facilitating tumor growth. Thus, targeting FoxP3+ Tregs could improve cancer immunotherapies. Here, we conducted a high-throughput, phenotypic screening of a drug repurposing library to identify compounds downregulating FoxP3 expression in human primary T cells. We identified the tyrosine kinase inhibitor bosutinib and the STAT3 inhibitor nifuroxazide effectively downregulating FoxP3 expression. To identify more potent compounds, structural analogs of these two compounds were searched and validated. These analogs were found to reduce FoxP3 expression in a similar- or more potent manner than the original hits. All compounds inhibited Treg suppressive functions and reduced the expression of Treg activation markers. Importantly, bosutinib disrupted FAK and CaMKII signaling more potently in Tregs, whilst nifuroxazide and its analog NA16 targeted STAT3 protein levels more effectively in Tregs. Additionally, bosutinib and NA16 targeted effector Tregs more effectively than other Treg subsets. In summary, bosutinib, nifuroxazide, and their analogs inhibited FoxP3 expression, Treg suppressive abilities, and Treg activation effectively, which could serve as tools for the improvement of current cancer immunotherapies.

PMID:39955647 | DOI:10.1002/eji.202451225

Asian Pac J Allergy Immunol. 2025 Feb 16. doi: 10.12932/AP-131223-1749. Online ahead of print.

ABSTRACT

BACKGROUND: Atopic dermatitis (AD) and food allergy (FA) often originate early in life. Gut microbiota interactions with the host immune system influence allergy development, yet the distinct gut microbiome and functional profiles in individuals with AD, FA, or both AD+FA remain underexplored.

OBJECTIVE: We investigated microbial colonization and proteomic profiles in infants with AD, FA, and AD+FA compared to age- and sex-matched controls from the Allergy Development in Early Life and Associated Factors in the Thai Birth Cohort (ALICE).

METHODS: Gut microbiomes from stool samples were analyzed using 16S sequencing, and proteomic analysis was conducted by liquid chromatography-tandem mass spectrometry.

RESULTS: The study included 16 AD, 5 FA, 5 AD+FA subjects, and 26 controls. AD+FA group exhibited the most severe dysbiosis. Enrichment of proteins involved in methionine biosynthesis in Bifidobacterium scardovii and high Erysipelotrichaceae colonization suggest a link to high-fat diets, known to reduce intestinal short-chain fatty acid and serotonin levels, contributing to allergies. Erysipelotrichaceae in AD+FA groups also expressed proteins related to histidine degradation. Low Bifidobacteriaceae levels were noted in FA and AD+FA, with more pathogenic strains colonized. Increased Bacteroidaceae in FA and AD+FA and Enterobacteriaceae in FA were detected. Pathways involving vitamin B1, a ligand for proliferator-activated receptor-γ (PPAR-γ) from Enterobacteriaceae could promote TH2 cells, type 2 innate lymphoid cells, and M2 macrophages, likely contribute to allergic inflammation.

CONCLUSIONS: AD+FA phenotype exhibited the most distinctive gut microbiome alterations, highlighting unique dysbiosis patterns. Microbiome biosynthesis pathways involving metabolism of methionine, histidine, serotonin, and vitamin B1 point to new targets for modifying or treating AD and FA.

PMID:39955638 | DOI:10.12932/AP-131223-1749

Cancer. 2025 Feb 15;131(4):e35768. doi: 10.1002/cncr.35768.

ABSTRACT

BACKGROUND: Borrmann type-4 (B-4) advanced gastric cancer is challenging to diagnose through routine endoscopy, leading to a poor prognosis. The objective of this study was to develop an artificial intelligence (AI)-based system capable of detecting B-4 gastric cancers using upper endoscopy.

METHODS: Endoscopic images from 259 patients who were diagnosed with B-4 gastric cancer and 595 controls who had benign conditions were retrospectively collected from Seoul National University Hospital for training and testing. Internal validation involved prospectively collected endoscopic videos from eight patients with B-4 gastric cancer and 148 controls. For external validation, endoscopic images and videos from patients with B-4 gastric cancer and controls at the Seoul National University Bundang Hospital were used. To calculate patient-based accuracy, sensitivity, and specificity, a diagnosis of B-4 was made for patients in whom greater than 50% of the images were identified as B-4 gastric cancer.

RESULTS: The accuracy of the patient-based diagnosis was highest in the internal image test set, with accuracy, sensitivity, and specificity of 93.22%, 92.86%, and 93.39%, respectively. The accuracy of the model in the internal validation videos, the external validation images, and the external validation videos was 91.03%, 91.86%, and 86.71%, respectively. Notably, in both the internal and external video sets, the AI model demonstrated 100% sensitivity for diagnosing patients who had B-4 gastric cancer.

CONCLUSIONS: An innovative AI-based model was developed to identify B-4 gastric cancer using endoscopic images. This AI model is specialized for the highly sensitive detection of rare B-4 gastric cancer and is expected to assist clinicians in real-time endoscopy.

PMID:39955610 | DOI:10.1002/cncr.35768

Cell Rep Methods. 2025 Feb 24;5(2):100990. doi: 10.1016/j.crmeth.2025.100990. Epub 2025 Feb 14.

ABSTRACT

The need for a deeper understanding of adverse drug reaction (ADR) mechanisms is vital for improving drug safety and repurposing. This study introduces Drug Adverse Reaction Mechanism Explainer (DREAMER), a network-based framework that uses a comprehensive knowledge graph to uncover molecular mechanisms underlying ADRs and disease phenotypes. By examining shared phenotypes of drugs and diseases and their effects on protein-protein interaction networks, DREAMER identifies proteins linked to ADR mechanisms. Applied to 649 ADRs, DREAMER identified molecular mechanisms for 67 ADRs, including ventricular arrhythmia and metabolic acidosis, and emphasized pathways like GABAergic signaling and coagulation proteins in personality disorders and intracranial hemorrhage. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes, emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

PMID:39954672 | DOI:10.1016/j.crmeth.2025.100990

Cell Rep Methods. 2025 Feb 10:100990. doi: 10.1016/j.crmeth.2025.100990. Online ahead of print.

ABSTRACT

The need for a deeper understanding of adverse drug reaction (ADR) mechanisms is vital for improving drug safety and repurposing. This study introduces Drug Adverse Reaction Mechanism Explainer (DREAMER), a network-based framework that uses a comprehensive knowledge graph to uncover molecular mechanisms underlying ADRs and disease phenotypes. By examining shared phenotypes of drugs and diseases and their effects on protein-protein interaction networks, DREAMER identifies proteins linked to ADR mechanisms. Applied to 649 ADRs, DREAMER identified molecular mechanisms for 67 ADRs, including ventricular arrhythmia and metabolic acidosis, and emphasized pathways like GABAergic signaling and coagulation proteins in personality disorders and intracranial hemorrhage. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes, emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

PMID:39954672 | DOI:10.1016/j.crmeth.2025.100990

Expert Opin Drug Discov. 2025 Feb 15. doi: 10.1080/17460441.2025.2465373. Online ahead of print.

ABSTRACT

INTRODUCTION: Bladder cancer presents a significant health problem worldwide, with environmental and genetic factors contributing to its incidence. Histologically, it can be classified as carcinoma in situ, non-muscle invasive and muscle-invasive carcinoma, each one with distinct genetic alterations impacting prognosis and response to therapy. While traditional transurethral resection is commonly performed in carcinoma in situ and non-muscle invasive carcinoma, it often fails to prevent recurrence or progression to more aggressive phenotypes, leading to the frequent need for additional treatment such as intravesical chemotherapy or immunotherapy. Despite the advances made in recent years, treatment options for bladder cancer are still lacking due to the complex nature of this disease. So, animal models may hold potential for addressing these limitations, because they not only allow the study of disease progression but also the evaluation of therapies and the investigation of drug repositioning.

AREAS COVERED: This review discusses the use of animal models over the past decade, highlighting key discoveries and discussing advantages and disadvantages for new drug discovery.

EXPERT OPINION: Over the past decade animal models have been employed to evaluate new mechanisms underlying the responses to standard therapies, aiming to optimize bladder cancer treatment. The authors propose that molecular engineering techniques and AI may hold promise for the future development of more precise and effective targeted therapies in bladder cancer.

PMID:39954010 | DOI:10.1080/17460441.2025.2465373

Cancer Chemother Pharmacol. 2025 Feb 15;95(1):34. doi: 10.1007/s00280-025-04759-8.

ABSTRACT

PURPOSE: In 20-30% of the patients, fluoropyrimidines (5-FU) based chemotherapy leads to severe toxicity, which is associated with dihydropyridine dehydrogenase (DPD) deficiency. Therefore, DPYD genotyping became standard practice before treatment with fluoropyrimidines. Nevertheless, only 17% of the patients with severe toxicity have a DPYD variant. Therefore, an urgent need persists to investigate other strategies contributing to prediction and prevention of toxicity. Endogenous DPD substrates are considered as potential biomarkers to predict toxicity, yet contradictional data exist on demonstrating uracil as a reliable biomarker. Thymine as biomarker for toxicity has been investigated less. The aim of this study was to determine the association between the concentrations of uracil, thymine dihydrouracil (DHU) and dihydrothymine (DHT), with the systemic drug exposure of 5-FU and DPD enzyme activity in patients treated with 5-FU.

METHODS: We included 36 patients with gastrointestinal malignancy who received 5-FU infusion. DPYD genotyping was conducted before start of treatment. Blood samples for determining 5-FU, uracil and thymine concentrations during infusion and DPD enzyme activity were taken.

RESULTS: We found a significant correlation between the 5-FU systematic exposure and baseline thymine concentrations (R2 = 0.1468; p = 0.0402). DPD enzyme activity was significantly correlated with baseline thymine concentrations but no correlation was found between DPD enzyme activity and 5-FU systemic drug exposure.

CONCLUSION: 5-FU dose individualization based on thymine concentrations could be a promising addition to DPYD genotyping to predict 5-FU-induced toxicity. Larger prospective trials are needed to examine thymine as predictor for toxicity in daily practice.

TRIAL REGISTRATION: Trial NL7539 at 'Overview of Medical Research in the Netherlands' (ID NL-OMON21471). Date of registration 19-02-2019.

PMID:39955449 | DOI:10.1007/s00280-025-04759-8

J Genet Genomics. 2025 Feb 13:S1673-8527(25)00038-4. doi: 10.1016/j.jgg.2025.02.001. Online ahead of print.

ABSTRACT

Founder events influence recessive diseases in highly endogamous populations. Several Indian populations have experienced significant founder events due to strict endogamy. However, the clinical implications of it remain underexplored. Therefore, we perform whole-exome sequencing of 281 individuals from four South Indian populations, characterized by high IBD scores. Our study reveals a high inbreeding rate of 59% across the populations. We identify ∼29.2% of the variants that are exclusively present in a single population and uncovered 1284 unreported exonic variants, underscoring the underrepresentation of Indian populations in global databases. Among these, 23 are predicted to be deleterious, all present in heterozygous state may be pathogenic when homozygous, an expected phenomenon in endogamous populations. Approximately 16%-33% of the identified pathogenic variants showed significantly higher occurrence rates compared to the South Asian populations from 1000 Genomes dataset. Pharmacogenomic analysis revealed distinct allele frequencies of variants in CYP450 and non-CYP450 genes, highlighting heterogeneous drug responses and associated risks. We report a high prevalence of ankylosing spondylitis in Reddy population, linked to HLA-B*27:04 allele and strong founder effect. Our findings highlight the need for extensive genomic research in understudied Indian populations for better understanding of disease risk and evolving strategies for precision and preventive medicine.

PMID:39955025 | DOI:10.1016/j.jgg.2025.02.001

J Psychiatr Res. 2025 Feb 6;183:106-111. doi: 10.1016/j.jpsychires.2025.02.005. Online ahead of print.

ABSTRACT

INTRODUCTION: Although post-COVID major depressive disorder (MDD) is frequent, the physiological mechanisms associated with it remain unclear. This study aimed to assess the association between 10 residual blood markers of inflammation and the presence of MDD 4 months after the acute phase of COVID-19.

METHODS: This is a cross-sectional study of the COMEBAC cohort that followed patients 4 months after hospitalization for COVID-19 at Bicêtre Hospital. Patients with lingering symptoms or who had been in critical care (n = 177) were invited to a day hospital for assessment of MDD and peripheral inflammation. Ten peripheral inflammatory markers were examined: plasmatic C-reactive protein; leukocyte, monocyte, neutrophil, and lymphocyte counts; the neutrophil to lymphocyte ratio; the systemic inflammatory index (i.e., the (platelet x neutrophil) to lymphocyte ratio); cortisol, ferritin, and hemoglobin levels. Current MDD was assessed through structured interviews with a psychiatrist, depressive symptoms through self-questionnaires. Peripheral inflammatory markers were compared between patients with post-COVID MDD and patients without a lifetime history of psychiatric disorders (controls).

RESULTS: Out of 177 patients, 24 (13.6%) had MDD. No significant differences in peripheral inflammatory markers were observed between patients with post-COVID MDD and controls. Furthermore, peripheral inflammatory markers were not correlated with symptoms of depression.

CONCLUSION: We found no association between post-COVID MDD and 10 peripheral inflammatory markers 4 months after COVID-19 infection. Other potential mechanisms warrant investigation.

PMID:39954540 | DOI:10.1016/j.jpsychires.2025.02.005

Lancet Respir Med. 2025 Feb 12:S2213-2600(25)00017-7. doi: 10.1016/S2213-2600(25)00017-7. Online ahead of print.

NO ABSTRACT

PMID:39954705 | DOI:10.1016/S2213-2600(25)00017-7

BMC Oral Health. 2025 Feb 15;25(1):246. doi: 10.1186/s12903-025-05588-0.

ABSTRACT

OBJECTIVES: This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.

METHODS: This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset. Eight clinical classifications were employed, including sagittal and vertical skeletal facial patterns, maxillary and mandibular anteroposterior positions, inclinations of upper and lower incisors, as well as their anteroposterior positions. The images were manually annotated for initially classification, which was verified by senior orthodontists. The data were randomly divided into training, validation, and test sets at a ratio of approximately 8:1:1. The multitask learning classification model was constructed based on the ResNeXt50_32 × 4d network and consisted of shared layers and task-specific layers. The performance of the model was evaluated using classification accuracy, precision, sensitivity, specificity and area under the curve (AUC).

RESULTS: This model could perform eight clinical diagnostic classifications on cephalograms within an average of 0.0096 s. The accuracy of the six classifications was 0.8-0.9, and the accuracy of the two classifications was 0.75-0.8. The overall AUC values for each classification exceeded 0.9.

CONCLUSIONS: An automatic diagnostic classification model for lateral cephalograms was established based on multitask learning to achieve simultaneous classification of eight common clinical diagnostic items. The multitask learning model achieved better classification performance and reduced the computational costs, providing a novel perspective and reference for addressing such problems.

PMID:39955570 | DOI:10.1186/s12903-025-05588-0

BMC Med Inform Decis Mak. 2025 Feb 15;25(1):81. doi: 10.1186/s12911-025-02923-x.

ABSTRACT

The diagnosis of pancreatic cancer presents a significant challenge due to the asymptomatic nature of the disease and the fact that it is frequently detected at an advanced stage. This study presents a novel approach combining graph-based data representation with DARTS-optimised MobileViT models, with the objective of enhancing diagnostic accuracy and reliability. The images of the pancreatic CT were transformed into graph structures using the Harris Corner Detection algorithm, which enables the capture of complex spatial relationships. Subsequently, the graph representations were processed using MobileViT models that had been optimised with Differentiable Architecture Search (DARTS), thereby enabling dynamic architectural adaptation. To further enhance classification accuracy, advanced machine learning algorithms, including K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forest (RF), and XGBoost, were applied. The MobileViTv2_150 and MobileViTv2_200 models demonstrated remarkable performance, with an accuracy of 97.33% and an F1 score of 96.25%, surpassing the capabilities of traditional CNN and Vision Transformer models. This innovative integration of graph-based deep learning and machine learning techniques demonstrates the potential of the proposed method to establish a new standard for early pancreatic cancer diagnosis. Furthermore, the study highlights the scalability of this approach for broader applications in medical imaging, which could lead to improved patient outcomes.

PMID:39955532 | DOI:10.1186/s12911-025-02923-x

Int J Comput Assist Radiol Surg. 2025 Feb 15. doi: 10.1007/s11548-024-03290-0. Online ahead of print.

ABSTRACT

OBJECTIVE: BRAFV600E is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic features and utilize deep transfer learning (DTL) from ultrasound images to develop a noninvasive artificial intelligence model for identifying BRAFV600E mutations.

MATERIALS AND METHODS: Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL-radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM).

RESULTS: Sole reliance on radiomics for identification of BRAFV600E mutations had limited capability, but the optimal DTLR model, combined with ResNet152, effectively identified BRAFV600E mutations. In the validation set, the AUC, accuracy, sensitivity and specificity were 0.833, 80.6%, 76.2% and 81.7%, respectively. The AUC of the DTLR model was higher than that of the DTL and radiomics models. Visualization using the ResNet152-based DTLR model revealed its ability to capture and learn ultrasound image features related to BRAFV600E mutations.

CONCLUSION: The ResNet152-based DTLR model demonstrated significant value in identifying BRAFV600E mutations in patients with PTC using ultrasound images. Grad-CAM has the potential to objectively stratify BRAF mutations visually. The findings of this study require further collaboration among more centers and the inclusion of additional data for validation.

PMID:39955452 | DOI:10.1007/s11548-024-03290-0

NPJ Digit Med. 2025 Feb 15;8(1):105. doi: 10.1038/s41746-025-01496-3.

ABSTRACT

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.

PMID:39955424 | DOI:10.1038/s41746-025-01496-3

Sci Rep. 2025 Feb 15;15(1):5656. doi: 10.1038/s41598-025-89533-9.

ABSTRACT

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases and first aid. However, accurate interpretation of respiratory sounds using a stethoscope is a subjective process that requires considerable expertise from clinicians. To overcome the shortcomings of existing stethoscopes, research is actively being conducted to develop an artificial intelligence deep learning model that can interpret breathing sounds recorded through electronic stethoscopes. Most recent studies in this area have focused on CNN-based respiratory sound classification models. However, such CNN models are limited in their ability to accurately interpret conditions that require longer overall length and more detailed context. Therefore, in the present work, we apply the Transformer model-based Audio Spectrogram Transformer (AST) model to our actual clinical practice data. This prospective study targeted children who visited the pediatric departments of two university hospitals in South Korea from 2019 to 2020. A pediatric pulmonologist recorded breath sounds, and a pediatric breath sound dataset was constructed through double-blind verification. We then developed a deep learning model that applied the pre-trained weights of the AST model to our data with a total of 194 wheezes and 531 other respiratory sounds. We compared the performance of the proposed model with that of a previously published CNN-based model and also conducted performance tests using previous datasets. To ensure the reliability of the proposed model, we visualized the classification process using Score-Class Activation Mapping (Score-CAM). Our model had an accuracy of 91.1%, area under the curve (AUC) of 86.6%, precision of 88.2%, recall of 76.9%, and F1-score of 82.2%. Ultimately, the proposed transformer-based model showed high accuracy in wheezing detection, and the decision-making process of the model was also verified to be reliable. The artificial intelligence deep learning model we have developed and described in this study is expected to help accurately diagnose pediatric respiratory diseases in real-world clinical practice.

PMID:39955399 | DOI:10.1038/s41598-025-89533-9

Sci Rep. 2025 Feb 15;15(1):5633. doi: 10.1038/s41598-025-89333-1.

ABSTRACT

Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, and dyspnea. The study aimed to improve the classification of HF subtypes and identify potential drug targets by exploring the role of Immunogenic Cell Death (ICD), a process known for its role in tumor immunity but underexplored in HF research. Additionally, the study sought to apply deep learning models to enhance HF classification and identify diagnosis-related genes. Various deep learning encoder models were employed to evaluate their effectiveness in clustering HF based on ICD-related genes. Identified HF subtypes were further refined using differentially expressed genes, allowing for the assessment of immune infiltration and functional enrichment. Advanced machine learning techniques were used to identify diagnosis-related genes, and these genes were used to construct nomogram models. The study also explored gene interactions with miRNA and transcription factors. Distinct HF subtypes were identified through clustering based on ICD-related genes. Differentially expressed genes revealed significant variations in immune infiltration and functional enrichment across these subtypes. The diagnostic model showed excellent performance, with an AUC exceeding 0.99 in both internal and external test sets. Diagnosis-related genes were also identified, serving as the foundation for nomogram models and further exploration of their regulatory interactions. This study provides a novel insight into HF by combining the exploration of ICD, the application of deep learning models, and the identification of diagnosis-related genes. These findings contribute to a deeper understanding of HF subtypes and highlight potential therapeutic targets for improving HF classification and treatment.

PMID:39955386 | DOI:10.1038/s41598-025-89333-1

Sci Rep. 2025 Feb 15;15(1):5672. doi: 10.1038/s41598-025-89916-y.

ABSTRACT

The exchange of information in Wireless Sensor Networks (WSNs) across different environments, whether they are above the ground, underground, underwater, or in space has advanced significantly over time. Among these advancements, precise localization of nodes within the network remains a key and vital challenge. In the context of Underwater Wireless Sensor Networks (UWSNs), localization plays a pivotal role in enabling the efficient execution of diverse underwater applications such as environmental monitoring, disaster management, military surveillance and many more. This review article is focusing on three primary aspects, the first section focuses on the fundamentals of localization in UWSNs, providing an in depth and comprehensive discussion on various localization methods. Where we have highlighted the two main categories that are anchor based and anchor free localization along with their respective subcategories. The second section of this article examines the diverse challenges that may emerge during the implementation of the localization process. To enhance clarity and structure, these challenges have been carefully analyzed and categorized into three main groups and that are, (i) Algorithmic challenges, (ii) Technical challenges, and (iii) Environmental challenges. The third section of this article begins by presenting the latest advancements in UWSNs localization, followed by an exploration of how Machine Learning (ML) and Deep Learning (DL) models can contribute in enhancing the localization process. To evaluate the potential benefits of the ML and DL techniques, we have assessed their performance through simulations, focusing on metrics such as localization error, velocity estimation error, Root Mean Square Error (RMSE), and energy consumption. This review also aims to provide actionable insights and a guideline for future research directions and opportunities for practitioners in the field of UWSNs localization. Which will ultimately help in enhancing the performance and reliability of underwater applications by advancing localization techniques and promoting seamless integration.

PMID:39955359 | DOI:10.1038/s41598-025-89916-y

Sci Rep. 2025 Feb 15;15(1):5654. doi: 10.1038/s41598-024-83346-y.

ABSTRACT

Total-body CT scans are useful in saving trauma patients; however, interpreting numerous images with varied window settings slows injury detection. We developed an algorithm for "unified total-body CT image with multiple organ-specific windowings (Uni-CT)", and assessing its impact on physician accuracy and speed in trauma CT interpretation. From November 7, 2008, to June 19, 2020, 40 cases of total-body CT images for blunt trauma with multiple injuries, were collected from the emergency department of Osaka General Medical Center and randomly divided into two groups. In half of the cases, the Uni-CT algorithm using semantic segmentation assigned visibility-friendly window settings to each organ. Four physicians with varying levels of experience interpreted 20 cases using the algorithm and 20 cases in conventional settings. The performance was analyzed based on the accuracy, sensitivity, specificity of the target findings, and diagnosis speed. In the proposal and conventional groups, patients had an average of 2.6 and 2.5 targeting findings, mean ages of 51.8 and 57.7 years, and male proportions of 60% and 45%, respectively. The agreement rate for physicians' diagnoses was κ = 0.70. Average accuracy, sensitivity, and specificity of target findings were 84.8%, 74.3%, 96.9% and 85.5%, 81.2%, 91.5%, respectively, with no significant differences. Diagnostic speed per case averaged 71.9 and 110.4 s in each group (p < 0.05). The Uni-CT algorithm improved the diagnostic speed of total-body CT for trauma, maintaining accuracy comparable to that of conventional methods.

PMID:39955327 | DOI:10.1038/s41598-024-83346-y

Sci Rep. 2025 Feb 15;15(1):5627. doi: 10.1038/s41598-025-88498-z.

ABSTRACT

This study aimed to generate a delayed 64Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acquired PET images from six patients with malignancies at 1, 24, and 48 h post-injection (p. i.) with 8 mCi 64Cu-DOTA-rituximab to fit a time-activity curve for dosimetry. We used a paired image-to-image translation (I2I) model based on a generative adversarial network to generate delayed images from early PET images. The image similarity function between the generated image and its ground truth was determined by comparing L1 and perceptual losses. We also applied organ-wise dosimetry to acquired and generated images using OLINDA/EXM. The quality of the generated images was good, even of tumors, when using the L1 loss function as an additional loss to the adversarial loss function. The organ-wise cumulative uptake and corresponding equivalent dose were estimated. Although the absorbed dose in some organs was accurately measured, predictions for organs associated with body clearance were relatively inaccurate. These results suggested that paired I2I can be used to alleviate burdensome dosimetry for radioimmunoconjugates.

PMID:39955298 | DOI:10.1038/s41598-025-88498-z

NPJ Syst Biol Appl. 2025 Feb 15;11(1):18. doi: 10.1038/s41540-025-00489-y.

ABSTRACT

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1-5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.

PMID:39955293 | DOI:10.1038/s41540-025-00489-y