Waste Manag. 2025 Aug 4;206:115046. doi: 10.1016/j.wasman.2025.115046. Online ahead of print.

ABSTRACT

Accurate estimation of the quantity of localized construction and demolition waste (CDW) is critical for optimizing the upstream operations of the CDW's reverse supply chain (RSC). However, existing studies extensively focus on downstream RSC operations with approaches that quantify large-scale material stockpiles through semi-automated workflows relying on expensive, non-portable devices. These approaches are impractical for upstream operations such as quantifying small-scale, localized CDW stockpiles scattered around urban environments, requiring frequent estimations. In contrast, this study proposes a novel vision-based framework that enables automated, fast, and accurate volume estimation of small-scale localized CDW using a consumer-grade imaging device. The framework incorporates a hybrid segmentation technique involving a ground plane identification process through a novel rule-based modification to the Random Sample Consensus (RANSAC) algorithm, followed by a clustering process. A new Multi-View Classification Model (MVCM) based on ResNet-50 architecture is also developed to recognize CDW clusters. A Delaunay triangulation-based approach estimates the volume of recognized CDW clusters. The framework is developed and validated using one of the most extensive datasets comprising 184 scans from the laboratory and the field environment. The MVCM achieved a high F1 score of 0.97 for identifying CDW using 3500 images. The framework demonstrates high accuracy for volume estimation, achieving an absolute percentage error (APE) of 8.97% compared to manual measurements. The overall process achieves an end-to-end processing time of 11 min, underscoring its efficiency and suitability for field deployment. The proposed framework is of significant practical value for localized CDW quantification and decision-making in upstream RSC operations.

PMID:40763364 | DOI:10.1016/j.wasman.2025.115046

PLoS One. 2025 Aug 5;20(8):e0329205. doi: 10.1371/journal.pone.0329205. eCollection 2025.

ABSTRACT

We propose a deep learning-based method for detecting Socially Desirable Responding (SDR)-the tendency for individuals to distort questionnaire responses to present themselves in a favorable light. Our objective is to showcase that such novel methods can be leveraged to design instruments that have the potential to measure this construct in an effective way. Participants' tendency to engage in SDR was initially modelled by specifying a latent variable model from Big Five personality scores, using data from 91 participants in a job application simulation (Big Five questionnaire and video introduction). Nonverbal visual cues (5,460 data points following data augmentation) were extracted from the participants' video presentations in form of sequences of images for training a transfer learning model designated as Entrans. The objective of Entrans is to discern patterns within these cues in order to detect whether sample participants manifest higher or lower SDR tendency. We conducted a regression-based prediction task to train and evaluate Entrans, resulting in a promising performance (MSE = .07, RMSE = .27, ρ = .27). A further analysis was conducted using a classification-based prediction task, which corroborated the potential of Entrans as a tool for detecting SDR (AUC = .71). These results were further analyzed by a Grad-CAM method to elucidate the underlying model behaviors. Findings suggest that the middle and lower parts of the face were the regions relied upon by Entrans to identify individuals with higher tendency of SDR in the classification task. These tentative interpretations give rise to the suggestion that socially desirable responding in a questionnaire and impression management in a job interview might share a common underlying cause. While the detection of SDR during personnel selection presents a significant challenge for organizations, our proof-of-concept demonstrates how machine learning might be leveraged to develop practical solutions as well as addressing theoretical questions.

PMID:40763162 | DOI:10.1371/journal.pone.0329205

IEEE Trans Image Process. 2025 Aug 5;PP. doi: 10.1109/TIP.2025.3594151. Online ahead of print.

ABSTRACT

In recent years, deep learning has shown immense promise in advancing medical hyperspectral imaging diagnostics at the microscopic level. Despite this progress, most existing research models remain constrained to single-task or single-scene applications, lacking robust collaborative interpretation of microscopic hyperspectral features and spatial information, thereby failing to fully explore the clinical value of hyperspectral data. In this paper, we propose a microscopic hyperspectral universal feature perception framework (UFPF), which extracts high-quality spatial-spectral features of hyperspectral data, providing a robust feature foundation for downstream tasks. Specifically, this innovative framework captures different sequential spatial nearest-neighbor relationships through a hierarchical corner-to-center mamba structure. It incorporates the concept of "progressive focus towards the center", starting by emphasizing edge information and gradually refining attention from the edges towards the center. This approach effectively integrates richer spatial-spectral information, boosting the model's feature extraction capability. On this basis, a dual-path spatial-spectral joint perception module is developed to achieve the complementarity of spatial and spectral information and fully explore the potential patterns in the data. In addition, a Mamba-attention Mix-alignment is designed to enhance the optimized alignment of deep semantic features. The experimental results on multiple datasets have shown that this framework significantly improves classification and segmentation performance, supporting the clinical application of medical hyperspectral data. The code is available at: https://github.com/Qugeryolo/UFPF.

PMID:40763051 | DOI:10.1109/TIP.2025.3594151

Cesk Patol. 2025;61(2):70-90.

ABSTRACT

The study focuses on the utilization of artificial intelligence (AI) algorithms in the diagnosis of breast, lung, and prostate cancer. It describes the historical development of the digitalization of pathological processes, the implementation of artificial intelligence, and its current applications in pathology. The study emphasizes machine learning, deep learning, computer vision, and digital pathology, which contribute to the automation and refinement of diagnostics. Special attention is given to specific tools such as the uPath systems from Roche and IBEX Medical Analytics, which enable the analysis of histopathological images, tumor cell classification, and biomarker evaluation. The study also highlights the benefits of AI utilization, including increased diagnostic accuracy and efficiency in laboratory processes, while simultaneously addressing the challenges associated with its implementation, such as ethical and legal considerations, data protection, and liability for errors. The aim of this study is to provide a comprehensive overview of the potential applications of AI in digital pathology and its role in modern oncological diagnostics.

PMID:40763009

J Clin Invest. 2025 Aug 5:e192923. doi: 10.1172/JCI192923. Online ahead of print.

ABSTRACT

The leukemia fusion gene CBFB-MYH11 requires RUNX1 for leukemogenesis, but the underlying mechanism is unclear. By in vitro studies, we found that CBFβ-SMMHC, the chimeric protein encoded by CBFB-MYH11, could enhance the binding affinity between RUNX1 and its target DNA. Increased RUNX1-DNA binding was also observed in myeloid progenitor cells from mice expressing CBFβ-SMMHC. Moreover, only CBFβ-SMMHC variants able to enhance the DNA binding affinity by RUNX1 could induce leukemia in mouse models. Marked transcriptomic changes, affecting genes associated with inflammatory response and target genes of CBFA2T3, were observed in mice expressing leukemogenic CBFβ-SMMHC variants. Finally, we show that CBFβ-SMMHC could not induce leukemia in mice with a Runx1-R188Q mutation, which reduces RUNX1 DNA binding but not affecting its interaction with CBFβ-SMMHC or its sequestration to cytoplasm by CBFβ-SMMHC. Our data suggest that, in addition to binding RUNX1 to regulate gene expression, enhancing RUNX1 binding affinity to its target DNA is an important mechanism by which CBFβ-SMMHC contributes to leukemogenesis, highlighting RUNX1-DNA interaction as a potential therapeutic target in inv(16) AML.

PMID:40763310 | DOI:10.1172/JCI192923

PLoS Pathog. 2025 Aug 5;21(8):e1013395. doi: 10.1371/journal.ppat.1013395. eCollection 2025 Aug.

NO ABSTRACT

PMID:40763154 | DOI:10.1371/journal.ppat.1013395

Pharmacotherapy. 2025 Aug 5. doi: 10.1002/phar.70041. Online ahead of print.

ABSTRACT

OBJECTIVES: Second-generation antipsychotic (SGA) medications are frequently prescribed for mental health conditions; however, they are associated with an increased risk of metabolic syndrome (MetS). We aimed to identify genetic associations of SGA-associated MetS (SGA-MetS) using genome-wide approaches within the UK Biobank. We also set out to evaluate if genetically predicted obesity is associated with an increased risk of SGA-MetS.

METHODS: We defined SGA-MetS based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria using cross-sectional data from 1318 UK Biobank participants who reported being on an SGA medication. An SGA-MetS case was defined as meeting three or more of the five NCEP-ATP III criteria. We performed a genome-wide association study (GWAS) and gene-based analysis to identify significant variants and gene associations. We computed the polygenic risk score (PGS) for body mass index (BMI) using 2,100,302 variants validated for obesity and metabolic traits from imputed single-nucleotide polymorphism (SNP) data. We tested the association of PGS-BMI with SGA-MetS using logistic regression.

RESULTS: GWAS identified suggestive associations (p < 1 × 10-5) on chromosome 15. The variant rs12914956 in CHD2 was associated with increased risk of SGA (odds ratio (OR) = 1.73, 95% confidence interval (CI) = 1.4-2.4, p = 3.6 × 10-7). The gene-based analysis identified significant gene associations with RBFOX1 (p = 4.85 × 10-7), PTPRD (p = 7.6 × 10-7), CSMD1 (p = 2.2 × 10-6), and CHD2 (p = 1.3 × 10-6). The PGS-BMI (β = 0.23, p = 6.8 × 10-5), was associated with increased MetS in a model adjusted for age, sex, physical activity, alcohol consumption, antidepressant medications, schizophrenia diagnosis, and principal components of ancestry.

CONCLUSION: Using a gene-based analysis, we identified significant gene associations with SGA-MetS that have been previously associated with obesity and metabolic traits. The PGS-BMI was associated with MetS, suggesting that a genetic predisposition to a higher BMI may increase the risk of SGA-MetS. Future research should replicate the findings in a larger dataset with more diverse populations.

PMID:40762455 | DOI:10.1002/phar.70041

J Imaging Inform Med. 2025 Aug 5. doi: 10.1007/s10278-025-01632-1. Online ahead of print.

ABSTRACT

Adequate withdrawal time is crucial in colonoscopy, as it is directly associated with polyp detection rates. However, traditional withdrawal time measurements can be biased by non-observation activities, leading to inaccurate assessments of procedural quality. This study aimed to develop a deep learning (DL) model that accurately measures net withdrawal time by excluding non-observation phases and generates quantitative visual summaries of key procedural events. We developed a DL-based automated temporal video segmentation model trained on 40 full-length colonoscopy videos and 825 cecum clips extracted from 221 colonoscopy procedures. The model classifies four key events: cecum, intervention, outside, and narrow-band imaging (NBI) mode. Using the temporal video segmentation results, we calculated the net withdrawal time and extracted representative images from each segment for video summarization. Model performance was evaluated using four standard temporal video segmentation metrics, and its correlation with endoscopist-recorded times on both internal and external test datasets. In both internal and external tests, the DL model achieved a total F1 score exceeding 93% for temporal video segmentation performance. The net withdrawal time showed a strong correlation with endoscopist-recorded times (internal dataset, r = 0.984, p < 0.000; external dataset, r = 0.971, p < 0.000). Additionally, the model successfully generated representative images, and the endoscopists' visual assessment confirmed that these images provided accurate summaries of key events. Compared to manual review, the proposed model offers a more efficient, standardized and objective approach to assessing procedural quality. This model has the potential to enhance clinical practice and improve quality assurance in colonoscopy.

PMID:40762931 | DOI:10.1007/s10278-025-01632-1

Mol Inform. 2025 Aug;44(8):e202500094. doi: 10.1002/minf.70005.

ABSTRACT

In computer-aided drug design, molecular generation models play a crucial role in accelerating the drug development process. Current models mainly fall into two categories: deep learning models with high performance but poor interpretability and heuristic algorithms with better interpretability but limited performance. In this study, we introduce an innovative molecular generation model, the compound construction model (CCMol), which integrates the powerful generative capabilities of the generative pretrained transformer (GPT) and the efficient optimization mechanisms of genetic algorithms (GA) to achieve effective and innovative molecular structures. Specifically, our approach uses structure-based drug design comprising both ligand and protein primary structure-based aspects. CCMol integrates GPT for initial molecular generation and GA for iterative optimization of physicochemical and biological properties. The model's reliability was validated by generating molecules targeting three critical disease-related proteins (GLP1, WRN, and JAK2). The results indicate that CCMol is on average with current advanced models in multiple indicators and performs better than the baseline model in terms of structure diversity and drug-related properties indicators, demonstrating that CCMol exhibits outstanding performance in developing novel and effective candidate drug molecules, particularly suitable for expanding the chemical validity of candidate structures at the early stages of drug discovery.

PMID:40762910 | DOI:10.1002/minf.70005

Radiology. 2025 Aug;316(2):e243393. doi: 10.1148/radiol.243393.

ABSTRACT

Background Sybil, an open-source deep learning model that uses low-dose CT (LDCT) for lung cancer prediction, requires rigorous external testing to confirm generalizability. Additionally, its utility in identifying individuals with high risk who never smoked or have light smoking histories remains unanswered. Purpose To externally test Sybil for identifying individuals with high risk for lung cancer within an Asian health checkup cohort. Materials and Methods This retrospective study analyzed LDCT scans from a single medical checkup facility in a study sample of individuals aged 50-80 years, collected between January 2004 and December 2021, with at least one follow-up scan. The predictive performance of the model for lung cancer risk over a 6-year period was assessed using the time-dependent area under the receiver operating characteristic curve (AUC). These evaluations were conducted in the overall study sample and within subgroups of patients with heavy (at least 20 pack-years) and never- or light smoking histories (ie, ever smoking [median, 2 pack-years]; ineligible for lung cancer screening per 2021 U.S. Preventive Services Task Force recommendations). Additionally, performance was evaluated according to the visibility of lung cancers on baseline LDCT scans. Results: Among 18 057 individuals (median age, 56 years [IQR, 52-61 years]; 11 267 male), 92 lung cancers were diagnosed (0.5%) within 6 years. Of these, 2848 had heavy smoking histories and 9943 had never- or light smoking histories, with 24 (0.8%) and 41 (0.4%) lung cancers, respectively. Sybil achieved AUCs of 0.91 for 1-year risk and 0.74 for 6-year risk. In the heavy-smoking subgroup, 1-year AUC was 0.94 (for visible lung cancers) and 6-year AUC was 0.70 (for future lung cancers). For the never- or light-smoking subgroup, Sybil had an AUC of 0.89 for visible lung cancers and 0.56 for future lung cancers. Conclusion: Sybil demonstrated excellent discriminative performance for visible lung cancers and acceptable performance for future lung cancers in Asian individuals with heavy smoking history but demonstrated poor performance for future lung cancers in a never- or light-smoking subgroup. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Jacobson and Byrne in this issue.

PMID:40762850 | DOI:10.1148/radiol.243393

Radiology. 2025 Aug;316(2):e243412. doi: 10.1148/radiol.243412.

ABSTRACT

Background Artificial intelligence may enhance diagnostic accuracy in classifying ovarian lesions on MRI scans; however, its applicability across diverse datasets is uncertain. Purpose To develop an efficient, generalizable pipeline for MRI-based ovarian lesion characterization. Materials and Methods In this retrospective study, multiparametric MRI datasets of patients with ovarian lesions from a primary institution (January 2008 to January 2019) and two external institutions (January 2010 to October 2020) were analyzed. Lesions were automatically segmented using Meta's Segment Anything Model (SAM). A DenseNet-121 deep learning (DL) model incorporating both imaging and clinical data was then trained and validated externally for ovarian lesion classification. Lesions were evaluated by radiologists using the Ovarian-Adnexal Reporting and Data System for MRI and subjective assessment, classifying them as benign or malignant. The classification performances of the DL model and radiologists were compared using the DeLong test. Results The primary dataset included 534 lesions from 448 women (mean age, 52 years ± 15 [SD]) from institution A (United States), whereas the external datasets included 58 lesions from 55 women (mean age, 51 years ± 19) from institution B (United States) and 29 lesions from 29 women (mean age, 49 years ± 10) from institution C (Taiwan). SAM-assisted segmentation had a Dice coefficient of 0.86-0.88, reducing the processing time per lesion by 4 minutes compared with manual segmentation. The DL classification model achieved an area under the receiver operating characteristic curve (AUC) of 0.85 (95% CI: 0.85, 0.85) on the internal test and 0.79 (95% CI: 0.79, 0.79 and 0.78, 0.79) across both external datasets with SAM-segmented images, comparable with the radiologists' performance (AUC: 0.84-0.93; all P > .05). Conclusion These results describe an accurate, efficient pipeline that integrates SAM with DL-based classification for differentiating malignant from benign ovarian lesions on MRI scans. It reduced segmentation time and achieved classification performance comparable with that of radiologists. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Bhayana and Wang in this issue.

PMID:40762846 | DOI:10.1148/radiol.243412

Int Ophthalmol. 2025 Aug 5;45(1):324. doi: 10.1007/s10792-025-03660-w.

ABSTRACT

OBJECTIVE: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidentified, and often takes a long time to get the right conclusions. This study suggested a new hybrid Deep Learning (DL) approach for retinal illness classification using retinal images to overcome these problems. Three crucial stages are included in this proposed study: preprocessing, feature extraction, and disease classification.

METHODS: At first, the retinal images are preprocessed using the Modified Gaussian Filtering technique to enhance the quality of the image. Subsequently, ResNet, VGG16-based feature descriptors are applied to the preprocessed image along with Improved Multi-Texton features, and statistical features are derived to obtain the most pertinent characteristics and minimize the dimensionality to boost the performance of the model. Then, these obtained features are employed in the hybrid classification model, which is a combination of an Improved LinkNet (ILinkNet) and SqueezeNet models. These models independently process the features for effective classification of disease. Lastly, the final classification results are obtained by averaging the outcomes of both classifiers.

RESULTS: Additionally, the efficiency of the proposed ILink-SqNet model is assessed in comparison to the current techniques. As a result, the ILink-SqNet model achieved a precision of 0.951, which surpasses the result of MobileNet (0.846), SpinalNet (0.821), CNN-Trans (0.836), and LinkNet (0.859), SqueezeNet (0.794) and Fundus-DeepNet (0.762) respectively.

CONCLUSION: Therefore, the suggested ILink-SqNet method provides a robust and effective solution for disease classification, ultimately contributing to better patient outcomes and more efficient clinical practices.

PMID:40762730 | DOI:10.1007/s10792-025-03660-w

Clin Oral Investig. 2025 Aug 5;29(8):396. doi: 10.1007/s00784-025-06485-0.

ABSTRACT

OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differentiate between skeletal classes I, II, and III in German orthodontic patients. This prospective cross-sectional study aimed to develop machine- and deep-learning models for diagnosing their skeletal class based on the gold-standard individualised ANB of Panagiotidis and Witt.

MATERIALS AND METHODS: Orthodontic patients treated in Germany contributed to the study population. Pre-treatment cephalometric parameters, sex, and age served as input variables. Machine-learning models performed were linear discriminant analysis (LDA), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), support vector machine (SVM), Gaussian naïve Bayes (NB), and multi class logistic regression (MCLR). Furthermore, an artificial neural network (ANN) was conducted.

RESULTS: 1277 German patients presented skeletal class I (48.79%), II (27.56%) and III (23.64%). The best machine-learning model, which considered all input parameters, was RF with 100% accuracy, with Calculated_ANB being the most important (0.429). The model with Calculated_ANB only achieved 100% accuracy (KNN), but ANB alone was inappropriate (71-76% accuracy). The ANN with all parameters and Calculated_ANB achieved 95.31% and 100% validation-accuracy, respectively.

CONCLUSIONS: Machine- and deep-learning methods can correctly determine an individual's skeletal class. Calculated_ANB was the most important among all input parameters, which, therefore, requires precise determination.

CLINICAL RELEVANCE: The AI methods introduced may help to establish digital and automated workflows in cephalometric diagnostics, which could contribute to the relief of the orthodontic practitioner.

PMID:40762676 | DOI:10.1007/s00784-025-06485-0

Anal Chem. 2025 Aug 5. doi: 10.1021/acs.analchem.5c02524. Online ahead of print.

ABSTRACT

Lung cancer, a leading cause of global cancer-related mortality, predominantly features nonsmall cell lung cancer (NSCLC), constituting 80% of all lung malignancies. Despite chemotherapy being the primary NSCLC treatment, the emergence of drug resistance poses a significant challenge. Identifying drug-resistant cells and characterizing the resistance type is crucial for guiding clinical interventions in NSCLC. The homogeneity of drug-sensitive/resistant cancer cells presents a challenge in their identification as well as in distinguishing tumor slices. Organelles, pivotal for cellular function, exhibit notable variations in the microenvironment among diverse cell types. In this work, three organelle-targeting nanoparticles, composed of fluorescent probes and polydopamine particles, collectively formed PPTA-SA (an organelle-targeting sensor array) for imaging NSCLC cells and tumor slices. With a deep learning network, PPTA-SA could be used for identification of drug-resistant lung cells and tumors. The achieved identification accuracy for drug-resistant NSCLC cells and NSCLC tumor slices was more than 99%. Moreover, the multiorganelle targeting photothermal therapy demonstrated superior tumor ablation effects compared to conventional single-organelle targeting photothermal therapy. The combination of fluorescent probes and polydopamine not only served as a valuable tool for drug-sensitive/resistant NSCLC identification but also facilitated photothermal therapy with enhanced effects.

PMID:40762433 | DOI:10.1021/acs.analchem.5c02524

ACS Appl Mater Interfaces. 2025 Aug 5. doi: 10.1021/acsami.5c11459. Online ahead of print.

ABSTRACT

Fibrillar protein hydrogels are promising sustainable biomaterials for biomedical applications, but their practical use is often limited by insufficient mechanical strength and stability. To address these challenges, we transformed native proteins into amyloid fibrils (AFs) and incorporated a fibrillar polysaccharide, phytagel (PHY), to engineer interpenetrating polymer network (IPN) hydrogels. Notably, we report for the first time the formation of an amyloid-based hydrogel from apoferritin (APO), with PHY reinforcing the network's mechanical integrity. In situ self-assembly of APO within the PHY matrix yields fully natural, biopolymer-based IPNs. Rheological analyses confirm synergistic interactions between AF and PHY fibers, with the composite hydrogels exhibiting significantly enhanced viscoelastic moduli compared with individual components. The AF-PHY hydrogels also demonstrate excellent self-healing behavior, rapidly restoring their storage modulus after high-strain deformation. A major advancement of this study is the application of deep learning (DL)-based image analysis, using convolutional neural networks, to automate the identification, segmentation, and quantification of fibrillar components in high-resolution scanning electron microscopy images. This AI-driven method enables precise differentiation between AF and PHY fibers and reveals the three-dimensional microarchitecture of the IPN, overcoming key limitations of traditional image analysis. Complementary real-time confocal laser scanning microscopy, with selective fluorescent labeling of protein and polysaccharide components, further validates the IPN structure of the hybrid hydrogels. Our results demonstrate that DL significantly enhances structural characterization and provides insights into gelation processes. This approach sets a new guide for the analysis of complex soft materials and underlines the potential of AF-PHY hydrogels as mechanically robust, self-healing, and fully sustainable biomaterials for biomedical engineering applications.

PMID:40762431 | DOI:10.1021/acsami.5c11459

Proteins. 2025 Aug 5. doi: 10.1002/prot.70030. Online ahead of print.

ABSTRACT

Biomolecular structure prediction has reached an unprecedented level of accuracy, partly attributed to the use of advanced deep learning algorithms. We participated in the CASP16 experiments across the categories of protein domains, protein multimers, and RNA monomers, achieving official rankings of first, second, and fourth (top for server groups), respectively. We hypothesized that by leveraging state-of-the-art structure predictors such as AlphaFold2, AlphaFold3, trRosettaX2, and trRosettaRNA2, accurate structure predictions could be achieved through careful optimization of input information. For protein structure prediction, we enhanced the input sequences by removing intrinsically disordered regions, a simple yet effective approach that yielded accurate models for protein domains. However, fewer than 25% of the protein multimers were predicted with high quality. In RNA structure prediction, optimizing the secondary structure input for trRosettaRNA2 resulted in more accurate predictions than AlphaFold3. In summary, our prediction results in CASP16 indicate that protein domain structure prediction has achieved high accuracy. However, predicting protein multimers and RNA structures remains challenging, and we anticipate new advancements in these areas in the coming years.

PMID:40762404 | DOI:10.1002/prot.70030

Magn Reson Med. 2025 Aug 5. doi: 10.1002/mrm.70013. Online ahead of print.

ABSTRACT

PURPOSE: Systematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images.

METHODS: Different averaging strategies, including windowed and interleaved averaging methods, and different levels of averaging before and after convolutional neural network-based and transformer-based denoising were studied. The experiments were performed on 152 single-delay ASL scans from 152 subjects, including pulsed and pseudo-continuous ASL acquisitions. Four-fold cross-validation was implemented in all experiments. The effect of including calibration scans (M0) was studied and compared across images of different levels of signal-to-noise ratio (SNR). The generalizability of DL denoising was examined in experiments using low-SNR ground truth in training. The results were assessed using image-quality metrics including structural similarity, peak SNR, and normalized mean absolute error.

RESULTS: Including M0 was almost always beneficial, with a dependence on the SNR of the input ASL images. Windowed averaging outperformed interleaved averaging, supporting the practice of reducing scan time. Averaging of ASL images before DL denoising was more advantageous than averaging after. Matching the SNR levels of the images in training and inferencing was important for optimal performance. These findings were consistent across convolutional neural network-based and transformer-based models. The generalizability of DL-based denoising was confirmed, and its capability to reduce artifacts was observed.

CONCLUSION: This study supports the use of DL-based denoising in improving the image quality of ASL and reducing scan time and provides insights to help optimize DL-denoising pipelines.

PMID:40762194 | DOI:10.1002/mrm.70013

J Math Biol. 2025 Aug 5;91(3):25. doi: 10.1007/s00285-025-02253-6.

ABSTRACT

The complex and dynamic crosstalk between tumour and immune cells results in tumours that can exhibit distinct qualitative behaviours-elimination, equilibrium, and escape-and intricate spatial patterns, yet share similar cell configurations in the early stages. We offer a topological approach to analyse time series of spatial data of cell locations (including tumour cells and macrophages) in order to predict malignant behaviour. We propose four topological vectorisations specialised to such cell data: persistence images of Vietoris-Rips and radial filtrations at static time points, and persistence images for zigzag filtrations and persistence vineyards varying in time. To demonstrate the approach, synthetic data are generated from an agent-based model with varying parameters. We compare the performance of topological summaries in predicting-with logistic regression at various time steps-whether tumour niches surrounding blood vessels are present at the end of the simulation, as a proxy for metastasis (i.e., tumour escape). We find that both static and time-dependent methods accurately identify perivascular niche formation, significantly earlier than simpler markers such as the number of tumour cells and the macrophage phenotype ratio. We find additionally that dimension 0 persistence applied to macrophage data, representing multi-scale clusters of the spatial arrangement of macrophages, performs best at this classification task at early time steps, prior to full tumour development, and performs even better when time-dependent data are included; in contrast, topological measures capturing the shape of the tumour, such as tortuosity and punctures in the cell arrangement, perform best at intermediate and later stages. We analyse the logistic regression coefficients for each method to identify detailed shape differences between the classes.

PMID:40762719 | DOI:10.1007/s00285-025-02253-6

Biochem Soc Trans. 2025 Aug 5:BST20253058. doi: 10.1042/BST20253058. Online ahead of print.

ABSTRACT

Critical for the regulation of eukaryotic gene transcription is the assembly and interplay of general transcription factors (GTFs) with RNA polymerases (RNAPs), leading to the formation of pre-initiation complexes (PICs) as a rate-limiting step in transcription activation. Compared with RNAPII PIC assembly involving many GTFs, activators, and co-activators, RNAPIII PIC assembly is less complex, involving mainly the four GTFs TFIIIA, TFIIIB, TFIIIC, and snRNA activating protein complex with only a few additional factors. The RNAPIII-specific GTF TFIIIC is present in type I and II promoters. One prominent area of investigation has been the dynamic interaction between TFIIIC and its promoter elements, the varying affinities of TFIIIC toward these elements, and the flexible linker within TFIIIC. Additionally, evidence suggests that TFIIIC may play a dual role, acting as an assembly factor that positions TFIIIB during PIC formation and as a barrier during RNAPIII-mediated transcription. By summarizing recent structural, biochemical, and genomic data, this review explores the mechanisms by which RNAPIII-specific GTFs, with a focus on TFIIIC, dynamically regulate RNAPIII transcription.

PMID:40762516 | DOI:10.1042/BST20253058

J Intellect Disabil Res. 2025 Aug 5. doi: 10.1111/jir.70017. Online ahead of print.

ABSTRACT

OBJECTIVES: Kabuki syndrome (KS), Wiedemann-Steiner syndrome (WSS) and O'Donnell-Luria-Rodan (ODLURO) syndrome are rare disorders caused by pathogenic variants in histone lysine methyltransferases, specifically the KMT2 gene family. All of these disorders are commonly associated with intellectual disability. Recent studies found overlap between KS and WSS cognitive phenotypes, suggesting shared disease pathogenesis. In contrast, the neuropsychological profile of ODLURO remains largely unknown. This study examines the academic learning concerns across the syndromes to better understand their cognitive profiles and provide guidance for clinical care.

METHODS: Fifty caregivers participated in this study, 25 with a child with WSS (Mean age = 12.85 years, SD = 1.82), 14 with KS (Mean age = 12.06, SD = 5.91) and 11 with ODLURO (Mean age = 12.43, SD = 4.69). All caregivers completed the Colorado Learning Difficulties Questionnaire, a parent-screening inventory of learning/academic challenges, specifically in reading, math and spatial skills.

RESULTS: Results suggest shared deficits in spatial skills, but different patterns of academic learning concerns across syndromes. Those with WSS were rated to show unique challenges in math and spatial domains, while those with ODLURO show global difficulties across areas. Individuals with KS were rated to show the most significant challenges in spatial skills, but comparable reading and math concerns.

CONCLUSIONS: Study results support recent publications on the overlapping cognitive profile in WSS and KS, specifically with distinct deficits in visual spatial processing. In contrast, ODLURO is associated with more generalised cognitive difficulties that warrant further investigation. Disruption of KMT2 genes may have common and individual effects on neurodevelopment that necessitate cross-syndrome research to illuminate gene-brain-behaviour relationships.

PMID:40762104 | DOI:10.1111/jir.70017