Eur J Cancer. 2025 Mar 15;220:115367. doi: 10.1016/j.ejca.2025.115367. Online ahead of print.

ABSTRACT

BACKGROUND: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) could increase evidence-based treatment by generating additional treatment recommendations (TR). We aimed to develop such an AI system for urothelial carcinoma (UC) and renal cell carcinoma (RCC).

METHODS: Comprehensive data of patients with histologically confirmed UC and RCC who received MCC recommendations in the years 2015 - 2022 were transformed into machine readable representations. Development of a two-step process to train a classifier to mimic TR was followed by identification of superordinate and detailed categories of TR. Machine learning (CatBoost, XGBoost, Random Forest) and deep learning (TabPFN, TabNet, SoftOrdering CNN, FCN) techniques were trained. Results were measured by F1-scores for accuracy weights.

RESULTS: AI training was performed with 1617 (UC) and 880 (RCC) MCC recommendations (77 and 76 patient input parameters). The AI system generated fully automated TR with excellent F1-scores for UC (e.g. 'Surgery' 0.81, 'Anti-cancer drug' 0.83, 'Gemcitabine/Cisplatin' 0.88) and RCC (e.g. 'Anti-cancer drug' 0.92 'Nivolumab' 0.78, 'Pembrolizumab/Axitinib' 0.89). Explainability is provided by clinical features and their importance score. Finally, TR and explainability were visualized on a dashboard.

CONCLUSION: This study demonstrates for the first time AI-generated, explainable TR in UC and RCC with excellent performance results as a potential support tool for high-quality, evidence-based TR in MCC. The comprehensive technical and clinical development sets global reference standards for future AI developments in MCC recommendations in clinical oncology. Next, prospective validation of the results is mandatory.

PMID:40107091 | DOI:10.1016/j.ejca.2025.115367

Comput Biol Med. 2025 Mar 18;190:110029. doi: 10.1016/j.compbiomed.2025.110029. Online ahead of print.

ABSTRACT

BACKGROUND: Neurological disorders, particularly Parkinson's Disease (PD), are serious and progressive conditions that significantly impact patients' motor functions and overall quality of life. Accurate and timely diagnosis is still crucial, but it is quite challenging. Understanding the changes in the brain linked to PD requires using neuroimaging modalities like magnetic resonance imaging (MRI). Artificial intelligence (AI), particularly deep learning (DL) methods, can potentially improve the precision of diagnosis.

METHOD: In the current study, we present a novel approach that integrates T1-weighted structural MRI and rest-state functional MRI using multi-site-cum-multi-modality neuroimaging data. To maximize the richness of the data, our approach integrates deep feature-level fusion across these modalities. We proposed a custom multi-scale 2D Convolutional Neural Network (CNN) architecture that captures features at different spatial scales, enhancing the model's capacity to learn PD-related complex patterns.

RESULTS: With an accuracy of 97.12 %, sensitivity of 97.26 %, F1-Score of 97.63 %, Area Under the Curve (AUC) of 0.99, mean average precision (mAP) of 99.53 %, and Dice Coefficient of 0.97, the proposed Neuro_DeFused-Net diagnostic model performs exceptionally well. These results highlight the model's robust ability to distinguish PD patients from Controls (Normal), even across a variety of datasets and neuroimaging modalities.

CONCLUSIONS: Our findings demonstrate the transformational ability of AI-driven models to facilitate the early diagnosis of PD. The proposed Neuro_DeFused-Net model enables the rapid detection of health markers through fast analysis of complicated neuroimaging data. Thus, timely intervention and individualized treatment strategies lead to improved patient outcomes and quality of life.

PMID:40107026 | DOI:10.1016/j.compbiomed.2025.110029

Invest Radiol. 2025 Mar 20. doi: 10.1097/RLI.0000000000001179. Online ahead of print.

ABSTRACT

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This review summarizes key developments in imaging modalities, computational tools, and clinical applications, highlighting major breakthroughs in thoracic diseases-lung cancer, pulmonary nodules, interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), COVID-19 pneumonia, and pulmonary embolism-and outlining future directions.Artificial intelligence (AI)-driven computer-aided detection systems and radiomic analyses have notably improved the detection and classification of pulmonary nodules, while photon-counting detector CT (PCD-CT) and low-field MRI offer enhanced resolution or radiation-free strategies. For lung cancer, CT texture analysis and perfusion imaging refine prognostication and therapy planning. ILD assessment benefits from automated diagnostic tools and innovative imaging techniques, such as PCD-CT and functional MRI, which reduce the need for invasive diagnostic procedures while improving accuracy. In COPD, dual-energy CT-based ventilation/perfusion assessment and dark-field radiography enable earlier detection and staging of emphysema, complemented by deep learning approaches for improved quantification. COVID-19 research has underscored the clinical utility of chest CT, radiographs, and AI-based algorithms for rapid triage, disease severity evaluation, and follow-up. Furthermore, tuberculosis remains a significant global health concern, highlighting the importance of AI-assisted chest radiography for early detection and management. Meanwhile, advances in CT pulmonary angiography, including dual-energy reconstructions, allow more sensitive detection of pulmonary emboli.Collectively, these innovations demonstrate the power of merging novel imaging technologies, quantitative functional analysis, and AI-driven tools to transform thoracic disease management. Ongoing progress promises more precise and personalized diagnostic and therapeutic strategies for diverse thoracic diseases.

PMID:40106831 | DOI:10.1097/RLI.0000000000001179

JMIR Aging. 2025 Mar 19;8:e63686. doi: 10.2196/63686.

ABSTRACT

BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking.

OBJECTIVE: This study's main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder.

METHODS: In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes.

RESULTS: Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI.

CONCLUSIONS: Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population.

PMID:40106819 | DOI:10.2196/63686

Sci Adv. 2025 Mar 21;11(12):eadt3803. doi: 10.1126/sciadv.adt3803. Epub 2025 Mar 19.

ABSTRACT

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annually, primarily due to liver cancer and cirrhosis. More than 1.5 million people contract HCV each year, disproportionately affecting vulnerable populations, including American Indians and Alaska Natives (AI/AN). While direct-acting antivirals (DAAs) are highly effective, timely and accurate HCV diagnosis remains a challenge, particularly in resource-limited settings. The current two-step HCV testing process is costly and time-intensive, often leading to patient loss before treatment. Point-of-care (POC) HCV antigen (Ag) testing offers a promising alternative, but no FDA-approved test meets the required sensitivity and specificity. To address this, we developed a fully automated, smartphone-based POC HCV Ag assay using platinum nanoparticles, deep learning image processing, and microfluidics. With an overall accuracy of 94.59%, this cost-effective, portable device has the potential to reduce HCV-related health disparities, particularly among AI/AN populations, improving accessibility and equity in care.

PMID:40106555 | DOI:10.1126/sciadv.adt3803

PLoS One. 2025 Mar 19;20(3):e0319828. doi: 10.1371/journal.pone.0319828. eCollection 2025.

ABSTRACT

This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. The main goal was to design a recommendation system that leverages app attributes, especially descriptions, to provide users with relevant contextual suggestions. A comprehensive pre-processing regime was carried out, including one-hot encoding, standardisation, and feature engineering. The feature, "Rating_Reviews", was introduced to capture the cumulative influence of ratings and reviews. The variable 'Category' was chosen as a target to discern different health contexts such as 'Weight loss' and 'Medical'. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model. The results highlighted the efficiency of transfer learning, especially BERT, which achieved an accuracy of approximately 90% after hyperparameter tuning. A final recommendation system was designed, which uses cosine similarity to rank apps based on their relevance to user queries.

PMID:40106462 | DOI:10.1371/journal.pone.0319828

PLoS Genet. 2025 Mar 19;21(3):e1011420. doi: 10.1371/journal.pgen.1011420. Online ahead of print.

ABSTRACT

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder architecture and human scRNA-seq datasets, has demonstrated remarkable success in human transcriptome analysis. However, given the prominence of the mouse, Mus musculus, as a primary mammalian model in biological and medical research, there is an acute need for a mouse-specific version of Geneformer. In this study, we developed a mouse-specific Geneformer (mouse-Geneformer) by constructing a large transcriptome dataset consisting of 21 million mouse scRNA-seq profiles and pre-training Geneformer on this dataset. The mouse-Geneformer effectively models the mouse transcriptome and, upon fine-tuning for downstream tasks, enhances the accuracy of cell type classification. In silico perturbation experiments using mouse-Geneformer successfully identified disease-causing genes that have been validated in in vivo experiments. These results demonstrate the feasibility of analyzing mouse data with mouse-Geneformer and highlight the robustness of the Geneformer architecture, applicable to any species with large-scale transcriptome data available. Furthermore, we found that mouse-Geneformer can analyze human transcriptome data in a cross-species manner. After the ortholog-based gene name conversion, the analysis of human scRNA-seq data using mouse-Geneformer, followed by fine-tuning with human data, achieved cell type classification accuracy comparable to that obtained using the original human Geneformer. In in silico simulation experiments using human disease models, we obtained results similar to human-Geneformer for the myocardial infarction model but only partially consistent results for the COVID-19 model, a trait unique to humans (laboratory mice are not susceptible to SARS-CoV-2). These findings suggest the potential for cross-species application of the Geneformer model while emphasizing the importance of species-specific models for capturing the full complexity of disease mechanisms. Despite the existence of the original Geneformer tailored for humans, human research could benefit from mouse-Geneformer due to its inclusion of samples that are ethically or technically inaccessible for humans, such as embryonic tissues and certain disease models. Additionally, this cross-species approach indicates potential use for non-model organisms, where obtaining large-scale single-cell transcriptome data is challenging.

PMID:40106407 | DOI:10.1371/journal.pgen.1011420

Proc Natl Acad Sci U S A. 2025 Mar 25;122(12):e2409685122. doi: 10.1073/pnas.2409685122. Epub 2025 Mar 19.

ABSTRACT

PAS domains are ubiquitous sensory modules that transduce environmental signals into cellular responses through tandem PAS folds and PAS-associated C-terminal (PAC) motifs. While this conserved architecture underpins their regulatory roles, here we uncover a structural divergence in the metazoan PAS domain-regulated kinase (PASK). By integrating evolutionary-scale domain mapping with deep learning-based structural models, we identified two PAS domains in PASK, namely PAS-B and PAS-C, in addition to the previously known PAS-A domain. Unlike canonical PAS domains, the PAS fold and PAC motif in the PAS-C domain are spatially segregated by an unstructured linker, yet a functional PAS module is assembled through intramolecular interactions. We demonstrate that this assembly is nutrient responsive and serves to remodel the quaternary structure of PASK that positions the PAS-A domain near the kinase activation loop. This nutrient-sensitive spatial arrangement stabilizes the activation loop, enabling catalytic activation of PASK. These findings revealed an alternative mode of regulatory control in PAS sensory proteins, where the structural assembly of PAS domains links environmental sensing to enzymatic activity. By demonstrating that PAS domains integrate signals through dynamic structural rearrangements, this study broadens the understanding of their functional and regulatory roles and highlights potential opportunities for targeting PAS domain-mediated pathways in therapeutic applications.

PMID:40106358 | DOI:10.1073/pnas.2409685122

JMIR Form Res. 2025 Mar 19;9:e54803. doi: 10.2196/54803.

ABSTRACT

BACKGROUND: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research.

OBJECTIVE: This study aims to address the data scarcity issue by generating a large volume of labeled Chinese abstract sentences without manual annotation, thereby creating new training datasets. Additionally, we seek to develop more accurate text classification algorithms to improve the precision of Chinese medical abstract classification.

METHODS: We developed 3 training datasets (dataset #1, dataset #2, and dataset #3) and a test dataset to evaluate our model. Dataset #1 contains 15,000 abstract sentences translated from the PubMed dataset into Chinese. Datasets #2 and #3, each with 15,000 sentences, were generated using GPT-3.5 from 40,000 Chinese medical abstracts in the CSL database. Dataset #2 used titles and keywords for pseudolabeling, while dataset #3 aligned abstracts with category labels. The test dataset includes 87,000 sentences from 20,000 abstracts. We used SBERT embeddings for deeper semantic analysis and evaluated our model using clustering (SBERT-DocSCAN) and supervised methods (SBERT-MEC). Extensive ablation studies and feature analyses were conducted to validate the model's effectiveness and robustness.

RESULTS: Our experiments involved training both clustering and supervised models on the 3 datasets, followed by comprehensive evaluation using the test dataset. The outcomes demonstrated that our models outperformed the baseline metrics. Specifically, when trained on dataset #1, the SBERT-DocSCAN model registered an impressive accuracy and F1-score of 89.85% on the test dataset. Concurrently, the SBERT-MEC algorithm exhibited comparable performance with an accuracy of 89.38% and an identical F1-score. Training on dataset #2 yielded similarly positive results for the SBERT-DocSCAN model, achieving an accuracy and F1-score of 89.83%, while the SBERT-MEC algorithm recorded an accuracy of 86.73% and an F1-score of 86.51%. Notably, training with dataset #3 allowed the SBERT-DocSCAN model to attain the best with an accuracy and F1-score of 91.30%, whereas the SBERT-MEC algorithm also showed robust performance, obtaining an accuracy of 90.39% and an F1-score of 90.35%. Ablation analysis highlighted the critical role of integrated features and methodologies in improving classification efficiency.

CONCLUSIONS: Our approach addresses the challenge of limited datasets for Chinese medical abstract classification by generating novel datasets. The deployment of SBERT-DocSCAN and SBERT-MEC models significantly enhances the precision of classifying Chinese medical abstracts, even when using synthetic datasets with pseudolabels.

PMID:40106267 | DOI:10.2196/54803

IEEE Trans Pattern Anal Mach Intell. 2025 Mar 19;PP. doi: 10.1109/TPAMI.2025.3553051. Online ahead of print.

ABSTRACT

Point cloud semantic segmentation can enhance the understanding of the production environment and is a crucial component of vision tasks. The efficacy and generalization prowess of deep learning-based segmentation models are inherently contingent upon the quality and nature of the data employed in their training. However, it is often challenging to obtain data with inter-class balance, and training an intelligent segmentation network with the imbalanced data may cause cognitive bias. In this paper, a network framework InvSpaceNet is proposed, which generates an inverse feature space to alleviate the cognitive bias caused by imbalanced data. Specifically, we design a dual-branch training architecture that combines the superior feature representations derived from instance-balanced sampling data with the cognitive corrections introduced by the proposed inverse sampling data. In the inverse feature space of the point cloud generated by the auxiliary branch, the central points aggregated by class are constrained by the contrastive loss. To refine the class cognition in the inverse feature space, features are used to generate point cloud class prototypes through momentum update. These class prototypes from the inverse space are utilized to generate feature maps and structure maps that are aligned with the positive feature space of the main branch segmentation network. The training of the main branch is dynamically guided through gradients back propagated from different losses. Extensive experiments conducted on four large benchmarks (i.e., S3DIS, ScanNet v2, Toronto-3D, and SemanticKITTI) demonstrate that the proposed method can effectively mitigate point cloud imbalance issues and improve segmentation performance.

PMID:40106253 | DOI:10.1109/TPAMI.2025.3553051

IEEE Trans Med Imaging. 2025 Mar 19;PP. doi: 10.1109/TMI.2025.3552692. Online ahead of print.

ABSTRACT

Photoacoustic imaging (PAI) is a high-resolution biomedical imaging technology for the non-invasive detection of a broad range of chromophores at multiple scales and depths. However, limited by low chromophore concentration, weak signals in deep tissue, or various noise, the signal-to-noise ratio of photoacoustic images may be compromised in many biomedical applications. Although improvements in hardware and computational methods have been made to address this problem, they have not been readily available due to either high costs or an inability to generalize across different datasets. Here, we present a self-supervised deep learning method to increase the signal-to-noise ratio of photoacoustic images using noisy data only. Because this method does not require expensive ground truth data for training, it can be easily implemented across scanning microscopic and computed tomographic data acquired with various photoacoustic imaging systems. In vivo results show that our method makes the vascular details that were completely submerged in noise become clearly visible, increases the signal-to-noise ratio by up to 12-fold, doubles the imaging depth, and enables high-contrast imaging of deep tumors. We believe this method can be readily applied to many preclinical and clinical applications.

PMID:40106247 | DOI:10.1109/TMI.2025.3552692

IEEE J Biomed Health Inform. 2025 Mar 19;PP. doi: 10.1109/JBHI.2025.3552819. Online ahead of print.

ABSTRACT

Cancer therapy-related cardiac dysfunction (CTRCD) is a potential complication associated with cancer treatment, particularly in patients with breast cancer, requiring monitoring of cardiac health during the treatment process. Tissue Doppler imaging (TDI) is a remarkable technique that can provide a comprehensive reflection of the left ventricle's physiological status. We hypothesized that the combination of TDI features with deep learning techniques could be utilized to predict CTRCD. To evaluate the hypothesis, we developed a temporal-multimodal pattern network for efficient training (TPNET) model to predict the incidence of CTRCD over a 24-month period based on TDI, function, and clinical data from 270 patients. Our model achieved an area under curve (AUC) of 0.83 and sensitivity of 0.88, demonstrating greater robustness compared to other existing visual models. To further translate our model's findings into practical applications, we utilized the integrated gradients (IG) attribution to perform a detailed evaluation of all the features. This analysis has identified key pathogenic signs that may have remained unnoticed, providing a viable option for implementing our model in preoperative breast cancer patients. Additionally, our findings demonstrate the potential of TPNET in discovering new causative agents for CTRCD.

PMID:40106240 | DOI:10.1109/JBHI.2025.3552819

JAMA Netw Open. 2025 Mar 3;8(3):e250991. doi: 10.1001/jamanetworkopen.2025.0991.

NO ABSTRACT

PMID:40105846 | DOI:10.1001/jamanetworkopen.2025.0991

JAMA Netw Open. 2025 Mar 3;8(3):e250984. doi: 10.1001/jamanetworkopen.2025.0984.

ABSTRACT

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms.

OBJECTIVE: To evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were graded via adjudication by US ophthalmologists for DR and DME, and ARDA's output was compared against the adjudicated grades at 45 sites in Southern India. Patients were randomly selected between January 1, 2019, and July 31, 2023.

MAIN OUTCOMES AND MEASURES: Primary analyses were the sensitivity and specificity of ARDA for severe nonproliferative DR (NPDR) or proliferative DR (PDR). Secondary analyses focused on sensitivity and specificity for sight-threatening DR (STDR) (DME or severe NPDR or PDR).

RESULTS: Among the 4537 patients with 4537 images with adjudicated grades, mean (SD) age was 55.2 (11.9) years and 2272 (50.1%) were male. Among the 3941 patients with gradable photographs, 683 (17.3%) had any DR, 146 (3.7%) had severe NPDR or PDR, 109 (2.8%) had PDR, and 398 (10.1%) had STDR. ARDA's sensitivity and specificity for severe NPDR or PDR were 97.0% (95% CI, 92.6%-99.2%) and 96.4% (95% CI, 95.7%-97.0%), respectively. Positive predictive value (PPV) was 50.7% and negative predictive value (NPV) was 99.9%. The clinically important miss rate for severe NPDR or PDR was 0% (eg, some patients with severe NPDR or PDR were interpreted as having moderate DR and referred to clinic). ARDA's sensitivity for STDR was 95.9% (95% CI, 93.0%-97.4%) and specificity was 94.9% (95% CI, 94.1%-95.7%); PPV and NPV were 67.9% and 99.5%, respectively.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study investigating the clinical performance of ARDA, sensitivity and specificity for severe NPDR and PDR exceeded 96% and caught 100% of patients with severe NPDR and PDR for ophthalmology referral. This preliminary large-scale postmarketing report of the performance of ARDA after screening 600 000 patients in India underscores the importance of monitoring and publication an algorithm's clinical performance, consistent with recommendations by regulatory bodies.

PMID:40105843 | DOI:10.1001/jamanetworkopen.2025.0984

J Chem Theory Comput. 2025 Mar 19. doi: 10.1021/acs.jctc.4c01136. Online ahead of print.

ABSTRACT

A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefore been of great interest to the simulation community. Here, we developed a general method for identifying progress coordinates "on-the-fly" during weighted ensemble (WE) rare-event sampling via deep learning (DL) of outliers among sampled conformations. Our method identifies outliers in a latent space model of the system's sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate the NTL9 protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our on-the-fly DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.

PMID:40105797 | DOI:10.1021/acs.jctc.4c01136

Technol Health Care. 2025 Mar 19:9287329241302736. doi: 10.1177/09287329241302736. Online ahead of print.

ABSTRACT

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

PMID:40105378 | DOI:10.1177/09287329241302736

J Chem Inf Model. 2025 Mar 19. doi: 10.1021/acs.jcim.5c00199. Online ahead of print.

ABSTRACT

Cell-penetrating peptides (CPPs) are usually short oligopeptides with 5-30 amino acid residues. CPPs have been proven as important drug delivery vehicles into cells through different mechanisms, demonstrating their potential as therapeutic candidates. However, experimental screening and synthesis of CPPs could be time-consuming and expensive. Recently, numerous attempts have been made to develop computational methods as a cost-effective way for screening a number of potential CPP candidates. Despite significant advancements, current methods exhibit limited feature representation capabilities, thereby constraining the potential for further performance enhancements. In this study, we developed a deep learning framework called CPPCGM, which uses protein language models (PLMs) to identify and generate novel CPPs. There are two separate blocks in this framework: CPPClassifier and CPPGenerator. The former utilizes three pretrained models for simple voting, thereby accurately categorizing CPPs and non-CPPs. The latter, similar to a generative adversarial network, including a discriminator and a generator, generates peptides that are not present in the training data set. Our proposed CPPCGM has achieved remarkably high Matthews correlation coefficient scores of 0.876, 0.923, and 0.664 on three data sets based on the classification results. Compared with the state-of-the-art methods, the performance of our method is significantly improved. The results also demonstrated the generating potential of CPPCGM through qualitative and quantitative evaluation of the generated samples. Significantly, using PLM-based methods can optimize peptides for biochemical functions, benefiting drug delivery and biomedical applications. Materials related are publicly available at https://github.com/QiufenChen/CPPCGM.

PMID:40105337 | DOI:10.1021/acs.jcim.5c00199

Eur J Prosthodont Restor Dent. 2025 Mar 13. doi: 10.1922/EJPRD_2801Hassan09. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an artificial intelligence system for automated classification of partially edentulous arches from panoramic radiographs using the Kennedy classification system and Applegate's rules, alongside identifying existing teeth for automated reporting.

METHODS: From 5261 anonymized digital panoramic radiographs collected from publicly available datasets, 1875 high-quality images were selected and divided into training (80%), validation (10%), and testing (10%) sets. Teeth were manually annotated on the Roboflow platform following the Universal Numbering System. To enhance model robustness, data augmentation techniques were applied, expanding the dataset to 2398 images. For tooth detection, a YOLOv8s deep learning model was trained for 80 epochs (batch size: 16, learning rate: 0.01). Performance was evaluated using precision, recall, F1 score, and mean average precision. Detected teeth were used to classify partially edentulous areas based on the Kennedy system. Modification areas were identified by analyzing detected and missing teeth, measuring bounded distances in millimetres, and classifying free-end saddle gaps.

RESULTS: The YOLOv8s model achieved a mean average precision (mAP50) of 98.1% for tooth identification, with precision and recall of 95.7% and 95.8%, respectively. For Kennedy classification, the model demonstrated precision of 0.962, recall of 0.931, and an F1-score of 0.939 across maxillary and mandibular arches.

CONCLUSIONS: The high accuracy and efficiency of this AI-driven approach can standardize classification, reduce diagnostic variability, and alleviate the workload for dental professionals, enabling seamless integration into clinical practice.

CLINICAL RELEVANCE: This AI system provides a consistent, accurate, and reliable method for classifying partially edentulous arches from panoramic radiographs, reducing manual assessment variability, easing practitioner workload, and enabling large-scale analysis of partial edentulism prevalence.

PMID:40105321 | DOI:10.1922/EJPRD_2801Hassan09

mSystems. 2025 Mar 19:e0018325. doi: 10.1128/msystems.00183-25. Online ahead of print.

ABSTRACT

In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pathways, and possess substantial commercial value. Research indicates that some synthetic promoters have higher transcriptional activity compared to strong natural promoters. However, with the exponential increase in complexity due to the 4n potential combinations in a promoter sequence of length n, identifying effective synthetic promoters remains a formidable challenge. Deep learning models, by adaptively learning from extensive data sets, have become instrumental in analyzing biological data. This study introduces a diffusion model-based approach for designing promoters viable in model bacteria such as Escherichia coli and cyanobacteria. This model proficiently assimilates and utilizes inherent biological features from natural promoter sequences to engineer synthetic variants. Additionally, we employed a transformer model to evaluate the efficacy of these synthetic promoters, aiming at screening those with high performance. The experimental findings suggest that the synthetic promoters by the diffusion model not only share key biological features with their natural counterparts but also demonstrate greater similarity to natural promoters than those generated by a variational autoencoder. In predicting promoter strength, the transformer model demonstrated improved performance over the convolutional neural network. Finally, we developed an integrated platform for generating promoters and predicting their strength.

IMPORTANCE: We demonstrated that diffusion models are superior in accomplishing the promoter synthesis task compared to other state-of-the-art deep learning models. The effectiveness of our method was validated using data sets of Escherichia coli and cyanobacteria promoters, showing more stable and prompt convergence and more natural-like promoters than the variational autoencoder model. We extracted sequence information, dimer information, and position information from promoters and combined them with a transformer model to predict promoter strength. Our prediction results were more accurate than those obtained with a convolutional neural network model. Our in silico experiments systematically introduced mutations in promoter sequences and explored their contribution to promoter strength, highlighting the depth of learning in our model.

PMID:40105319 | DOI:10.1128/msystems.00183-25

Nanoscale. 2025 Mar 19. doi: 10.1039/d4nr05031b. Online ahead of print.

ABSTRACT

Bismuth selenide (Bi2Se3) has emerged as a promising material for high-performance photodetectors due to its wideband spectral response, strong in-plane anisotropy, narrow bandgap, high absorption coefficient, and carrier mobility. However, inherent defects and states in Bi2Se3-based devices reduce optical conversion efficiency and stability. To address these challenges, we report the design and preparation of Bi2Se2.33S0.67 nanowires by a facile chemical vapor transport method. The individual Bi2Se2.33S0.67 nanowire photodetectors exhibit remarkable photoresponse over a broadband wavelength region ranging from ultraviolet C (254 nm) to near-infrared (1064 nm) with a low dark current of 0.015 nA and the measured maximum photoresponsivity of 2.52 A W-1 at 532 nm, together with a detectivity of around 5.2 × 1011 Jones. Furthermore, the photoresponse of photodetectors exhibits polarization angle sensitivity within a broadband range of 355 to 808 nm. The structural anisotropy of the Bi2Se2.33S0.67 crystal leads to a maximum dichroic ratio of about 1.8 at 355 nm. Additionally, cat images produced by this device further demonstrate the potential of the high-performance devices, and the effectiveness of photodetectors in deep learning image recognition validates their wide-spectrum, high-responsivity, and superior polarization-sensitive detection capabilities.

PMID:40105281 | DOI:10.1039/d4nr05031b