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

Cell Mol Life Sci. 2025 Feb 22;82(1):84. doi: 10.1007/s00018-025-05576-1.

ABSTRACT

Coenzyme A (CoA) is a crucial metabolite involved in various biological processes, encompassing lipid metabolism, regulation of mitochondrial function, and membrane modeling. CoA deficiency is associated with severe human diseases, such as Pantothenate Kinase-Associated Neurodegeneration (PKAN) and CoASY protein-associated neurodegeneration (CoPAN), which are linked to genetic mutations in Pantothenate Kinase 2 (PANK2) and CoA Synthase (CoASY). Although the association between CoA deficiency and mitochondrial dysfunction has been established, the underlying molecular alterations and mechanisms remain largely elusive. In this study, we investigated the detailed changes resulting from the functional decline of CoASY using the Drosophila model. Our findings revealed that a reduction of CoASY in muscle and brain led to degenerative phenotypes and apoptosis, accompanied by impaired mitochondrial integrity. The release of mitochondrial DNA was notably augmented, while the assembly and activity of mitochondrial electron transport chain (ETC) complexes, particularly complex I and III, were diminished. Consequently, this resulted in decreased ATP generation, rendering the fly more susceptible to energy insufficiency. Our findings suggest that compromised mitochondrial integrity and energy supply play a crucial role in the pathogenesis associated with CoA deficiency, thereby implying that enhancing mitochondrial integrity can be considered a potential therapeutic strategy in future interventions.

PMID:39985665 | DOI:10.1007/s00018-025-05576-1

J Agric Food Chem. 2025 Feb 22. doi: 10.1021/acs.jafc.4c12616. Online ahead of print.

ABSTRACT

Vibrio natriegens, a fast-growing bacterium, is an emerging chassis of next-generation industrial biotechnology capable of thriving under open and continuous culture conditions. Cadaverine, a valuable industrial C5 platform chemical, has various chemical and biological activities. This study found that V. natriegens exhibited superior tolerance to lysine, the substrate of cadaverine production. For the first time, a cadaverine synthesis pathway was introduced into V. natriegens for whole-cell catalysis of cadaverine from lysine. A high-efficiency cadaverine-producing strain harboring a toxin-antitoxin system, V. natriegens (pSEVA341-pTac-ldcC-pHbpBC-hbpBC) with lysE (PN96_RS17440) inactivation, was constructed. In 7 L bioreactors, the cadaverine titer increased from 115 g/L in the original strain to 158 g/L within 11 h of biotransformation, exhibiting a 37% increase in production. Its productivity reached 14.4 g/L/h with a conversion rate as high as 90%. These results confirm V. natriegens as an exceptional chassis for effective cadaverine bioproduction.

PMID:39985470 | DOI:10.1021/acs.jafc.4c12616

Exp Oncol. 2025 Feb 20;46(4):408-409. doi: 10.15407/exp-oncology.2024.04.408.

ABSTRACT

The Conference was organized on the initiative of the R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology of the National Academy of Sciences of Ukraine, the State Institution "SP Grigoriev Institute of Medical Radiology and Oncology of the National Academy of Medical Sciences of Ukraine", and public organizations "National Association of Oncologists of Ukraine" and "Ukrainian Society for Cancer Research". The cancer patient's health and the quality of life (QoL) was put in the focus of this conference. Various edges of cancer research were discussed by researchers together with medical doctors, clinical scientists, specialists in demography, economics, law, and the general public.

PMID:39985343 | DOI:10.15407/exp-oncology.2024.04.408

Naunyn Schmiedebergs Arch Pharmacol. 2025 Feb 22. doi: 10.1007/s00210-025-03899-1. Online ahead of print.

ABSTRACT

Studies on the assessment of anticancer efficacy of plant-derived phytochemicals by targeting signaling pathways have drawn a lot of attention recently for human health. Multiple investigations have proposed an involvement of Notch pathway in the processes of cancer angiogenesis and metastasis, and drug resistance. Moreover, overexpression of Notch signaling is associated with increased prostate cancer (PrCa) cell growth and development. A number of chemotherapeutic agents are reported to become resistant over a period of time and have severe side effects. To increase efficacy and lessen drug-induced toxicity, a variety of bioactive compounds have been utilized alone or as adjuncts to traditional chemotherapy. Therefore, in the present study, the potential of AKBA in inhibiting the proliferation of PrCa cells by modulating Notch signaling components and its efficacy in combination with cisplatin was investigated. The results exhibited a substantial reduction in cell survival (IC50 = 25.28 µM at 24 h and 16.50 µM at 48 h) and cellular alterations in AKBA-treated PrCa cells. Additionally, AKBA caused nuclear condensation, increased reactive oxygen species (ROS) generation, mitochondrial membrane depolarization, and caspase activation, ultimately leading to apoptosis in PrCa cells. Moreover, AKBA-elicited apoptosis was evidenced by an augmentation in the Bax to Bcl2 ratio. AKBA was also found to induce G0/G1 arrest which was substantiated by reduced cyclin D1 and CDK4 expression levels concomitantly with increased expression of p21 and p27 genes. Intriguingly, AKBA demonstrated significant downregulation of Notch signaling mediators. Furthermore, the isobolograms of the combination treatment indicated that AKBA has the potential to synergistically enhance the cytotoxic efficacy of cisplatin in DU145 cells, as evidenced by CI < 1 across all tested combinations. Overall, the results of this study suggest strong antiproliferative, apoptotic, and chemo-sensitizing potential of AKBA. Thus, AKBA holds a promising drug candidature warranting further investigation as a probable therapeutic option for both the prevention and treatment of PrCa and other solid tumors.

PMID:39985578 | DOI:10.1007/s00210-025-03899-1

Curr Med Res Opin. 2025 Feb;41(2):369-373. doi: 10.1080/03007995.2025.2470735. Epub 2025 Mar 4.

ABSTRACT

Patients with ultra rare cancers have a high unmet medical need for the development of safe and effective treatments. To advance cancer drug development is often considered economically unattractive, and usually infeasible with the use of traditional paradigms. Compounding the challenges, evolving scientific understanding of the molecular biology of cancers has resulted in further subdivision of rare cancers into small molecularly defined subsets that may be eligible for targeted therapies. Indeed, research in oncology has undergone an evolution due to advances in biomarker discovery and drug target innovation moving towards a more personalized medicine and effective approach to cancer treatment. These therapies have shown remarkable efficacy with better disease management and brought a higher quality of life for cancer patients. Given the rarity of the diseases, standard randomized controlled trials may not be feasible, and innovative study designs and statistical methods should be applied to evaluate new treatments. To this aim, regulatory agencies have developed guidelines to introduce flexibility in planning of clinical trials, including new adaptive designs, use of real-world data, and surrogate endpoints. This commentary aims at reporting challenges on the evaluation of new treatments for ultra rare cancers with a focus on innovative trial designs, statistical methods, and managing of patients as these cancers are often poorly understood, have limited clinical data, and may require specialized treatment approaches.

PMID:39980369 | DOI:10.1080/03007995.2025.2470735

Z Rheumatol. 2025 Mar;84(2):128-137. doi: 10.1007/s00393-025-01616-0. Epub 2025 Feb 21.

ABSTRACT

Metabolic bone diseases cause bone and joint pain and are manifested as rheumatism. Typical for the rare genetic disease hypophosphatasia is a reduced activity of alkaline phosphatase (AP), where the variable residual activity causes the heterogeneous symptoms (e.g., arthralgia, myalgia and fractures). It is indicated by repeatedly low AP measurements. The diagnosis requires a meticulous medical history and laboratory-based clarification in order to rule out other differential diagnoses. Although supportive measures form the basis of treatment, costly enzyme replacement therapy is a possible treatment option for severe forms. Multidisciplinary care under the direction of a rheumatologist experienced in osteology or an osteologist is crucial in order to provide adequate care to affected patients. Phosphate loss syndromes due to overactivity of fibroblast growth factor 23 (FGF-23) lead to deformities of the lower extremities and short stature (in congenital disorders), bone and muscle pain, muscular weakness and pathological fractures, depending on the time of occurrence during life. In genetic forms of the disease (especially X‑linked hypophosphatemia), supplementation with calcitriol and phosphates and, if necessary, complex corrective surgery in adolescence are traditional treatment methods, which are increasingly being replaced by treatment with antibodies against FGF-23. The acquired variant is a paraneoplastic phenomenon from small mostly benign mesenchymal tumors, which clinically shows a relatively acute course with severe bone pain, pathological fractures and muscle weakness in previously healthy patients and can ideally be cured by resection of the tumor. The disease can be suspected by significantly reduced serum phosphate levels and narrowed down with further laboratory diagnostics. In our opinion, the measurement of calcium, phosphate and alkaline phosphatase should be part of the primary laboratory diagnostics performed by rheumatologists and the follow-up of pathological findings is indicated.

PMID:39982479 | DOI:10.1007/s00393-025-01616-0

Sci Rep. 2025 Feb 21;15(1):6360. doi: 10.1038/s41598-025-85582-2.

ABSTRACT

As a high-risk production unit, natural gas supply enterprises are increasingly recognizing the need to enhance production safety management. Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. To address this issue, this study utilizes deep learning techniques to enhance the accuracy and reliability of natural gas load forecasting. By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep learning model for interval prediction of natural gas loads, which implements a graded alarm mechanism based on 85%, 90%, and 95% confidence intervals of real-time observations. This approach represents a novel strategy for enhancing enterprise safety production management. Experimental results demonstrate that the proposed method outperforms traditional warning models, reducing MAE, MAPE, MESE, and REMS by 1.13096 m3/h, 1.3504%, 7.6363 m3/h, 1.6743 m3/h, respectively, while improving R2 by 0.04698. These findings are expected to offer valuable insights for enhancing safe production management in the natural gas industry and provide new perspectives for the industry's digital and intelligent transformation.

PMID:39984509 | DOI:10.1038/s41598-025-85582-2

Nat Commun. 2025 Feb 21;16(1):1838. doi: 10.1038/s41467-025-57072-6.

ABSTRACT

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

PMID:39984438 | DOI:10.1038/s41467-025-57072-6