Sci Rep. 2025 Feb 22;15(1):6499. doi: 10.1038/s41598-025-91206-6.

ABSTRACT

Building rooftop extraction has been applied in various fields, such as cartography, urban planning, automatic driving, and intelligent city construction. Automatic building detection and extraction algorithms using high spatial resolution aerial images can provide precise location and geometry information, significantly reducing time, costs, and labor. Recently, deep learning algorithms, especially convolution neural networks (CNNs) and Transformer, have robust local or global feature extraction ability, achieving advanced performance in intelligent interpretation compared with conventional methods. However, buildings often exhibit scale variation, spectral heterogeneity, and similarity with complex geometric shapes. Hence, the building rooftop extraction results exist fragmentation and lack spatial details using these methods. To address these issues, this study developed a multi-scale global perceptron network based on Transformer and CNN using novel encoder-decoders for enhancing contextual representation of buildings. Specifically, an improved multi-head-attention encoder is employed by constructing multi-scale tokens to enhance global semantic correlations. Meanwhile, the context refinement decoder is developed and synergistically uses high-level semantic representation and shallow features to restore spatial details. Overall, quantitative analysis and visual experiments confirmed that the proposed model is more efficient and superior to other state-of-the-art methods, with a 95.18% F1 score on the WHU dataset and a 93.29% F1 score on the Massub dataset.

PMID:39987354 | DOI:10.1038/s41598-025-91206-6

Sci Rep. 2025 Feb 22;15(1):6501. doi: 10.1038/s41598-025-91364-7.

ABSTRACT

Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low coverage, there is limited investigation into the later stages of high coverage, which could impact the accuracy of forecasting yield. High canopy coverage and severe occlusion in later stages pose significant challenges for plant detection and counting. Therefore, this study evaluated the performance of the Count Crops tool and a deep learning (DL) model derived from early-stage unmanned aerial vehicle (UAV) imagery in detecting and counting Konjac plants during the high-coverage growth stage. Additionally, the study proposed an approach that integrates the DL model with Konjac location information from both early-stage and high canopy coverage stage imagery to improve the accuracy of recognizing Konjac plants during the high canopy coverage stage. The results indicated that the Count Crops tool outperformed the DL model constructed solely from early-stage imagery in detecting and counting Konjac plants during the high-coverage period. However, given the single stem and erect growth characteristics of Konjac, incorporating the DL model with the location information of the Konjac plants achieved the highest accuracy (Precision = 98.7%, Recall = 86.7%, F1-score = 92.3%). Our findings indicate that combining DL detection results from the early growth stages of Konjac, along with plant positional information from both growth stages, not only significantly improved the accuracy of detecting and counting plants but also saved time on annotating and training DL samples in the later stages. This study introduces an innovative approach for detecting and counting Konjac plants during high-coverage periods, providing a new perspective for recognizing and counting other crop plants at high-overlapping growth stages.

PMID:39987316 | DOI:10.1038/s41598-025-91364-7

NPJ Digit Med. 2025 Feb 22;8(1):120. doi: 10.1038/s41746-025-01491-8.

ABSTRACT

Hypertension is a major risk factor for cardiovascular disease (CVD), yet blood pressure is measured intermittently and under suboptimal conditions. We developed a deep learning model to identify hypertension and stratify risk of CVD using 12-lead electrocardiogram waveforms. HTN-AI was trained to detect hypertension using 752,415 electrocardiograms from 103,405 adults at Massachusetts General Hospital. We externally validated HTN-AI and demonstrated associations between HTN-AI risk and incident CVD in 56,760 adults at Brigham and Women's Hospital. HTN-AI accurately discriminated hypertension (internal and external validation AUROC 0.803 and 0.771, respectively). In Fine-Gray regression analyses model-predicted probability of hypertension was associated with mortality (hazard ratio per standard deviation: 1.47 [1.36-1.60], p < 0.001), HF (2.26 [1.90-2.69], p < 0.001), MI (1.87 [1.69-2.07], p < 0.001), stroke (1.30 [1.18-1.44], p < 0.001), and aortic dissection or rupture (1.69 [1.22-2.35], p < 0.001) after adjustment for demographics and risk factors. HTN-AI may facilitate diagnosis of hypertension and serve as a digital biomarker of hypertension-associated CVD.

PMID:39987256 | DOI:10.1038/s41746-025-01491-8

Sci Rep. 2025 Feb 22;15(1):6506. doi: 10.1038/s41598-025-90221-x.

ABSTRACT

Pathologists have depended on their visual experience to assess tissue structures in smear images, which was time-consuming, error-prone, and inconsistent. Deep learning, particularly Convolutional Neural Networks (CNNs), offers the ability to automate this procedure by recognizing patterns in tissue images. However, training these models necessitates huge amounts of labeled data, which can be difficult to come by due to the skill required for annotation and the unavailability of data, particularly for rare diseases. This work introduces a new semi-supervised method for tissue structure semantic segmentation in histopathological images. The study presents a CNN based teacher model that generates pseudo-labels to train a student model, aiming to overcome the drawbacks of conventional supervised learning approaches. Self-supervised training is used to improve the teacher model's performance on smaller datasets. Consistency regularization is integrated to efficiently train the student model on labeled data. Further, the study uses Monte Carlo dropout to estimate the uncertainty of proposed model. The proposed model demonstrated promising results by achieving an mIoU score of 0.64 on a public dataset, highlighting its potential to improve segmentation accuracy in histopathological image analysis.

PMID:39987243 | DOI:10.1038/s41598-025-90221-x

Sci Rep. 2025 Feb 22;15(1):6456. doi: 10.1038/s41598-025-90924-1.

ABSTRACT

To address the issue of serious inefficiency in the traditional manual evaluation methods of rock core integrity, a deep learning-based algorithm named IDA-RCF (Intelligent detection algorithm for Rock Core Fissure) is proposed in this paper, which realizes the automatic evaluation of rock core integrity in accordance with the fissure identification results. In IDA-RCF, a two-branch feature extraction network is firstly proposed, in which branch one is used to fully extract the complex and variable local detail fissure features by Deformable convolution, and branch two is used to capture the global context information of the rock core images by EfficientViT network based on the self-attention. Then a multi-level feature fusion network is proposed for adaptively fusing local and global features from the same level and the fused feature information from the previous level, thereby capturing more valid information and eliminating redundancies. Then the fused feature layer is decoded by the feature decoder to output the detection results of rock core fissure. Finally, the fissure rate is automatically calculated based on the detection results to predict the degree of rock core integrity. The experimental results show that the accuracy indexes F1, mAP@0.5 and mAP@0.5:0.95 of IDA-RCF are 93.09%, 94.44% and 84.61%, respectively. The relative error between the prediction results and the manual statistical results of the fissure rate is only 4.38%, and the prediction accuracy for the degree of rock core integrity is 93.8%, indicating that the proposed method in this paper is able to accomplish the intelligent evaluation task of rock core integrity with high precision.

PMID:39987183 | DOI:10.1038/s41598-025-90924-1

Sci Rep. 2025 Feb 22;15(1):6490. doi: 10.1038/s41598-025-91322-3.

ABSTRACT

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severity requires a radiologist with significant expertise. This study introduces a pioneering utilization of deep learning (DL) for evaluate COVID-19 severity using lung CT images, presenting a novel and effective method for assessing the severity of pulmonary manifestations in COVID-19 patients. Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. Inception-ResNet incorporates the dilated mechanism into its ResNet component, enhancing its ability to accurately classify lung involvement stages. This study demonstrates that dilated residual networks (dResNet) outperform their non-dilated counterparts in image classification tasks, as their architectural designs allow the systems to acquire comprehensive global data by expanding their receptive fields. Our study utilized an initial dataset of 1548 human thoracic CT scans, meticulously annotated by two experienced specialists. Lung involvement was determined by calculating a percentage based on observations made at each scan. The hybrid methodology successfully distinguished the ten distinct severity levels associated with COVID-19, achieving a maximum accuracy of 96.40%. This system demonstrates its effectiveness as a diagnostic framework for assessing lung involvement in COVID-19-affected individuals, facilitating disease progression tracking.

PMID:39987169 | DOI:10.1038/s41598-025-91322-3

J Transl Med. 2025 Feb 22;23(1):221. doi: 10.1186/s12967-025-06213-y.

ABSTRACT

BACKGROUND: Structural variations (SVs) are a pervasive and impactful class of genetic variation within the genome, significantly influencing gene function, impacting human health, and contributing to disease. Recent advances in deep learning have shown promise for SV detection; however, current methods still encounter key challenges in effective feature extraction and accurately predicting complex variations.

METHODS: We introduce SVEA, an advanced deep learning model designed to address these challenges. SVEA employs a novel multi-channel image encoding approach that transforms SVs into multi-dimensional image formats, improving the model's ability to capture subtle genomic variations. Additionally, SVEA integrates multi-head self-attention mechanisms and multi-scale convolution modules, enhancing its ability to capture global context and multi-scale features. The model was trained and tested on a diverse range of genomic datasets to evaluate its accuracy and generalizability.

RESULTS: SVEA demonstrated superior performance in detecting complex SVs compared to existing methods, with improved accuracy across various genomic regions. The multi-channel encoding and advanced feature extraction techniques contributed to the model's enhanced ability to predict subtle and complex variations.

CONCLUSIONS: This study presents SVEA, a deep learning model incorporating advanced encoding and feature extraction techniques to enhance structural variation prediction. The model demonstrates high accuracy, outperforming existing methods by approximately 4%, while also identifying areas for further optimization.

PMID:39987107 | DOI:10.1186/s12967-025-06213-y

Clin Lung Cancer. 2025 Jan 28:S1525-7304(25)00021-X. doi: 10.1016/j.cllc.2025.01.010. Online ahead of print.

ABSTRACT

INTRODUCTION: Sarcopenia is associated with worse survival in non-small cell lung cancer (NSCLC), but less studied in association with toxicity. Here, we investigated the association between imaging-assessed sarcopenia with toxicity in patients with NSCLC.

METHODS: We analyzed a "chemoradiation" cohort (n = 318) of patients with NSCLC treated with chemoradiation, and an external validation "chemo-surgery" cohort (n = 108) who were treated with chemotherapy and surgery from 2002 to 2013 at a different institution. A deep-learning pipeline utilized pretreatment computed tomography scans to estimate SM area at the third lumbar vertebral level. Sarcopenia was defined by dichotomizing SM index, (SM adjusted for height and sex). Primary endpoint was NCI CTCAE v5.0 grade 3 to 5 (G3-5) toxicity within 21-days of first chemotherapy cycle. Multivariable analyses (MVA) of toxicity endpoints with sarcopenia and baseline characteristics were performed by logistic regression, and overall survival (OS) was analyzed using Cox regression.

RESULTS: Sarcopenia was identified in 36% and 36% of patients in the chemoradiation and chemo-surgery cohorts, respectively. On MVA, sarcopenia was associated with worse G3-5 toxicity in chemoradiation (HR 2.00, P < .01) and chemo-surgery cohorts (HR 2.95, P = .02). In the chemoradiation cohort, worse OS was associated with G3-5 toxicity (HR 1.42, P = .02) but not sarcopenia on MVA. In chemo-surgery cohort, worse OS was associated with sarcopenia (HR 2.03, P = .02) but not G3-5 toxicity on MVA.

CONCLUSION: Sarcopenia, assessed by an automated deep-learning system, was associated with worse toxicity and survival outcomes in patients with NSCLC. Sarcopenia can be utilized to tailor treatment decisions to optimize adverse events and survival.

PMID:39986945 | DOI:10.1016/j.cllc.2025.01.010

Acta Biomater. 2025 Feb 20:S1742-7061(25)00136-9. doi: 10.1016/j.actbio.2025.02.042. Online ahead of print.

ABSTRACT

Platelets have long been established as a safeguard of our vascular system. Recently, haptotactic platelet migration has been discovered as a part of the immune response. In addition, platelets exhibit mechanosensing properties, changing their behavior in response to the stiffness of the underlying substrate. However, the influence of substrate stiffness on platelet migration behavior remains elusive. Here, we investigated the migration of platelets on fibrinogen-coated polydimethylsiloxane (PDMS) substrates with different stiffnesses. Using phase-contrast and fluorescence microscopy as well as a deep-learning neural network, we tracked single migrating platelets and measured their migration distance and velocity. We found that platelets migrated on stiff PDMS substrates (E = 2 MPa), while they did not migrate on soft PDMS substrates (E = 5 kPa). Platelets migrated also on PDMS substrates with intermediate stiffness (E = 100 kPa), but their velocity and the fraction of migrating platelets were diminished compared to platelets on stiff PDMS substrates. The straightness of platelet migration, however, was not significantly influenced by substrate stiffness. We used scanning ion conductance microscopy (SICM) to image the three-dimensional shape of migrating platelets, finding that platelets on soft substrates did not show the polarization and shape change associated with migration. Furthermore, the fibrinogen density gradient, which is generated by migrating platelets, was reduced for platelets on soft substrates. Our work demonstrates that substrate stiffness, and thus platelet mechanosensing, influences platelet migration. Substrate stiffness for optimal platelet migration is quite high (>100 kPa) in comparison to other cell types, with possible implications on platelet behavior in inflammatory and injured tissue. STATEMENT OF SIGNIFICANCE: Platelets can feel and react to the stiffness of their surroundings - a process called mechanosensation. Additionally, platelets migrate via substrate-bound fibrinogen as part of the innate immune response during injury or inflammation. It has been shown that the migration of immune cells is influenced by the stiffness of the underlying substrate, but the effect of substrate stiffness on the migration of platelets has not yet been investigated. Using differently stiff substrates made from PDMS, we show that substrate stiffness affects platelet migration. Stiff substrates facilitate fast and frequent platelet migration with a strong platelet shape anisotropy and a strong fibrinogen removal while soft substrates inhibit platelet migration. These findings highlight the influence of the stiffness of the surrounding tissue on the platelet immune response, possibly enhancing platelet migration in inflamed tissue.

PMID:39986637 | DOI:10.1016/j.actbio.2025.02.042

Photodiagnosis Photodyn Ther. 2025 Feb 20:104528. doi: 10.1016/j.pdpdt.2025.104528. Online ahead of print.

ABSTRACT

BACKGROUND: The most widespread primary intraocular tumor in adults is called uveal melanoma (UM), if detected early enough, it can be curable. Various methods are available to treat UM, but the most commonly used and effective approach is plaque radiotherapy using Iodine-125 and Ruthenium-106.

METHOD: The authors performed searches to distinguish relevant studies from 2017 to 2024 by three databases (PubMed, Scopus, and Google Scholar).

RESULTS: Imaging technologies such as Ultrasound (US), Fundus Photography (FP), Optical Coherent Tomography (OCT), Fluorescein Angiography (FA), and Magnetic Resonance Images (MRI) play a vital role in the diagnosis and prognosis of UM. The present review assessed the power of different image modalities when integrated with artificial intelligence (AI) to diagnose and prognosis of patients affected by UM.

CONCLUSION: Finally, after reviewing the studies conducted, it was concluded that AI is a developing tool in image analysis and enhances workflows in diagnosis from data and image processing to clinical decisions, improving tailored treatment scenarios, response prediction, and prognostication.

PMID:39986588 | DOI:10.1016/j.pdpdt.2025.104528

Br J Cancer. 2025 Feb 22. doi: 10.1038/s41416-025-02958-x. Online ahead of print.

ABSTRACT

Acute lymphoblastic leukaemia (ALL) is a rare and heterogeneous disease. The ALLTogether consortium has implemented a treatment protocol to improve outcome and reduce treatment-related toxicity across much of Europe. The consortium provides the opportunity to design translational research on patient material stored in national biobanks. However, there are currently no standardized guidelines for the types of material, processing, and storage for leukaemia biobanking. To address this gap, we conducted a modified Delphi survey among 53 experts in different roles related to leukaemia. The first round consisted of 63 statements asking for level of agreement. The second round refined some to reach consensus, using yes-no and multiple-option answers. Key recommendations include cryopreservation of cells from diagnosis, post-induction, post-consolidation, and relapse, with at least two aliquots of plasma and serum, and cerebrospinal fluid from diagnosis, day15, and post-induction. It was advised to distribute cells across multiple vials for various research projects, and to collect data on sample processing, cell viability, and blast percentage. Quality monitoring and user feedback were strongly recommended. The Delphi survey resulted in strong recommendations that can be used by national biobanks to harmonize storage of samples from patients with ALL and ensure high-quality cryopreserved cells for research studies.

PMID:39987377 | DOI:10.1038/s41416-025-02958-x

Commun Biol. 2025 Feb 22;8(1):279. doi: 10.1038/s42003-025-07594-y.

ABSTRACT

The identification of microglia subtypes is important for understanding the role of innate immunity in neurodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements to produce well-separated cell clusters. However, identification of subtypes can be obscured by gene expression noise, which diminishes the distances in transcriptome space between distinct cell types, blurs boundaries, and reduces reproducibility. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima that correspond to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in an inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimer's disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment, and are depleted in an anti-inflammatory subtype.

PMID:39987247 | DOI:10.1038/s42003-025-07594-y

Cell Genom. 2025 Feb 16:100775. doi: 10.1016/j.xgen.2025.100775. Online ahead of print.

ABSTRACT

Most GWAS loci are presumed to affect gene regulation; however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we map eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental-like tissues. Through colocalization, we annotate 10.4% (n = 540) of GWAS loci in 15 traits by QTL phenotype, temporal specificity, and complexity. We show that integration of chromatin QTLs results in a 2.3-fold higher annotation rate of GWAS loci because they capture distal GWAS loci missed by eQTLs, and that 5.4% (n = 13) of GWAS colocalizing eQTLs are early developmental specific. Finally, we utilize the iPSCORE multiomic QTLs to prioritize putative causal variants overlapping transcription factor motifs to elucidate the potential genetic underpinnings of 296 GWAS-QTL colocalizations.

PMID:39986281 | DOI:10.1016/j.xgen.2025.100775

Artif Intell Med. 2025 Feb 19;162:103090. doi: 10.1016/j.artmed.2025.103090. Online ahead of print.

ABSTRACT

New drug discovery has always been a costly, time-consuming process with a high failure rate. Repurposing existing drugs offers a valuable alternative and reduces the risks associated with developing new drugs. Various experimental methods have been employed to facilitate drug repositioning; however, associations prediction between drugs and diseases through biological experiments is both expensive and time-consuming. Consequently, it is imperative to develop efficient and highly precise computational methods for predicting these associations. Based on this, we propose a drug-disease associations prediction method based on Hyperbolic Multivariate feature Learning in High-order Heterogeneous Networks for Drug-Disease Prediction, called H3ML. Our approach begins by mining high-order information from protein-disease and drug-protein networks to construct high-order heterogeneous networks. Subsequently, we employ multivariate feature learning to create hyperbolic representations, and then enhance the features of the heterogeneous network. Finally, we utilize a hyperbolic graph attention network in the hyperbolic space to aggregate neighbor information and perform the final prediction task. In addition, we evaluate the performance of H3ML by comparing it with some state-of-the-art methods across different datasets. The case study further validate the effectiveness of H3ML. Our implementation will be publicly available at: https://github.com/jianruichen/H-3ML.

PMID:39985835 | DOI:10.1016/j.artmed.2025.103090

Comput Med Imaging Graph. 2025 Feb 19;121:102498. doi: 10.1016/j.compmedimag.2025.102498. Online ahead of print.

ABSTRACT

Brain tumors are a disease that kills thousands of people worldwide each year. Early identification through diagnosis is essential for monitoring and treating patients. The proposed study brings a new method through intelligent computational cells that are capable of segmenting the tumor region with high precision. The method uses deep learning to detect brain tumors with the "You only look once" (Yolov8) framework, and a fine-tuning process at the end of the network layer using intelligent computational cells capable of traversing the detected region, segmenting the edges of the brain tumor. In addition, the method uses a classification pipeline that combines a set of classifiers and extractors combined with grid search, to find the best combination and the best parameters for the dataset. The method obtained satisfactory results above 98% accuracy for region detection, and above 99% for brain tumor segmentation and accuracies above 98% for binary classification of brain tumor, and segmentation time obtaining less than 1 s, surpassing the state of the art compared to the same database, demonstrating the effectiveness of the proposed method. The new approach proposes the classification of different databases through data fusion to classify the presence of tumor in MRI images, as well as the patient's life span. The segmentation and classification steps are validated by comparing them with the literature, with comparisons between works that used the same dataset. The method addresses a new generative AI for brain tumor capable of generating a pre-diagnosis through input data through Large Language Model (LLM), and can be used in systems to aid medical imaging diagnosis. As a contribution, this study employs new detection models combined with innovative methods based on digital image processing to improve segmentation metrics, as well as the use of Data Fusion, combining two tumor datasets to enhance classification performance. The study also utilizes LLM models to refine the pre-diagnosis obtained post-classification. Thus, this study proposes a Computer-Aided Diagnosis (CAD) method through AI with PDI, CNN, and LLM.

PMID:39985841 | DOI:10.1016/j.compmedimag.2025.102498

Proteins. 2025 Feb 22. doi: 10.1002/prot.26810. Online ahead of print.

ABSTRACT

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this study, we developed a heuristic optimization method to enhance key functionalities such as solubility, flexibility, and stability, while preserving the structural integrity of proteins. This method aims to reduce laboratory demands by enabling a design that is both functional and structurally sound. This approach is particularly valuable for the synthetic production of proteins with anti-inflammatory properties and those used in gene therapy. The designed proteins were initially evaluated for their ability to preserve natural structures using recovery and confidence metrics, followed by assessments with the AlphaFold tool. Additionally, natural protein sequences were mutated using a genetic algorithm and compared with those designed by our method. The results demonstrate that the protein sequences generated by our method exhibit much greater similarity to native protein sequences and structures. The code and sequences for the designed proteins are available at https://github.com/aysenursoyturk/HMHO.

PMID:39985803 | DOI:10.1002/prot.26810

Plant J. 2025 Feb;121(4):e70019. doi: 10.1111/tpj.70019.

ABSTRACT

Sugarcane is a crop that accumulates sucrose with high photosynthesis efficiency. Therefore, the feedback regulation of sucrose on photosynthesis is crucial for improving sugarcane yield. Saccharum spontaneum and Saccharum officinarum are the two founding Saccharum species for modern sugarcane hybrids. S. spontaneum exhibits a higher net photosynthetic rate but lower sucrose content than S. officinarum. However, the mechanism underlying the negative feedback regulation of photosynthesis by sucrose remains poorly understood. This study investigates the effects of exogenous sucrose treatment on S. spontaneum and S. officinarum. Exogenous sucrose treatment increases sucrose content in the leaf base but inhibits photosynthetic efficiency and the expression of photosynthesis-related pathway genes (including RBCS and PEPC) in both species. However, gene expression patterns differed significantly, with few differentially expressed genes (DEGs) shared between the two species, indicating a differential response to exogenous sucrose. The expression networks of key genes involved in sugar metabolism, sugar transport, and PEPC and RBCS showed divergence between two species. Additionally, DEGs involved in the pentose phosphate pathway and the metabolism of alanine, aspartate, and glutamate metabolism were uniquely enriched in S. spontaneum, potentially contributing to the differential changes in sucrose content in the tip between the two species. We propose a model of the mechanisms underlying the negative feedback regulation of photosynthesis by sucrose in the leaves of S. spontaneum and S. officinarum. Our findings enhance the understanding of sucrose feedback regulation on photosynthesis and provide insights into the divergent molecular mechanisms of sugar accumulation in Saccharum.

PMID:39985806 | DOI:10.1111/tpj.70019

Proteins. 2025 Feb 22. doi: 10.1002/prot.26810. Online ahead of print.

ABSTRACT

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this study, we developed a heuristic optimization method to enhance key functionalities such as solubility, flexibility, and stability, while preserving the structural integrity of proteins. This method aims to reduce laboratory demands by enabling a design that is both functional and structurally sound. This approach is particularly valuable for the synthetic production of proteins with anti-inflammatory properties and those used in gene therapy. The designed proteins were initially evaluated for their ability to preserve natural structures using recovery and confidence metrics, followed by assessments with the AlphaFold tool. Additionally, natural protein sequences were mutated using a genetic algorithm and compared with those designed by our method. The results demonstrate that the protein sequences generated by our method exhibit much greater similarity to native protein sequences and structures. The code and sequences for the designed proteins are available at https://github.com/aysenursoyturk/HMHO.

PMID:39985803 | DOI:10.1002/prot.26810

STAR Protoc. 2025 Feb 21;6(1):103649. doi: 10.1016/j.xpro.2025.103649. Online ahead of print.

ABSTRACT

The tri-methylation of histone H3 on K4 (H3K4me3) is a key epigenetic modification that is predominantly found at active gene promoters and is deposited by the complex of proteins associated with SET1 (COMPASS). CXXC zinc finger protein 1 (Cfp1) regulates this process by recruiting SET1 to chromatin and recognizing H3K4me3 via its plant homeodomain (Cfp1PHD). Here, we present a protocol for the purification and crystallization of the Drosophila melanogaster Cfp1PHD domain in complex with an H3K4me3 peptide (PDB: 9C0O). We describe steps for obtaining highly pure Cfp1PHD and diffraction-quality crystals. We then detail procedures for rapidly identifying crystals containing the H3K4me3-bound form of the Cfp1PHD domain. For complete details on the use and execution of this protocol, please refer to Grégoire et al.1.

PMID:39985772 | DOI:10.1016/j.xpro.2025.103649

Expert Opin Ther Targets. 2025 Feb 22. doi: 10.1080/14728222.2025.2471579. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease with a dismal prognosis. While the standard-of-care (SOC) drugs approved for IPF represent a significant advancement in antifibrotic therapies, they primarily slow disease progression and have limited overall efficacy and many side effects. Consequently, IPF remains a condition with high unmet medical and pharmacological needs.

AREAS COVERED: A wide variety of molecules and mechanisms have been implicated in the pathogenesis of IPF, many of which have been targeted in clinical trials. In this review, we discuss the latest therapeutic targets that affect extracellular matrix (ECM) homeostasis and the activation of lung fibroblasts, with a specific focus on ECM invasion.

EXPERT OPINION: A promising new approach involves targeting ECM invasion by fibroblasts, a process that parallels cancer cell behavior. Several cancer drugs are now being tested in IPF for their ability to inhibit ECM invasion, offering significant potential for future treatments. The delivery of these therapies by inhalation is a promising development, as it may enhance local effectiveness and minimize systemic side effects, thereby improving patient safety and treatment efficacy.

PMID:39985559 | DOI:10.1080/14728222.2025.2471579