BMC Cancer. 2025 Jan 31;25(1):182. doi: 10.1186/s12885-025-13580-8.

ABSTRACT

BACKGROUND: Small-molecule compounds that even partially restore the GTPase activity of RASG12V can be used in anticancer therapy. Until now, attempts to obtain such compounds have failed. Compounds with this ability have been defined in our research.

METHODS: The compounds were initially identified through virtual screening, and their optimal binding conformation in the RAS SW-II pocket was determined using the flexible docking technique. Efficacy was verified based on the IC50 determination, GTPase activity, as well as the AKT and ERK phospho WB assays.

RESULTS: The IC50 of the tested compounds was significantly lower against cells with the RASG12V mutation than against selected types of normal cells. The molecular mechanism of action of these compounds was proposed - minimization of the negative impact of the V12 sidechain on GTP hydrolysis of RASG12V. The work also indicates that the model of action of RAS mutants in cell lines is incomplete. The analysed cell line (SW-480) with RAS mutations does not always show increased ERK and AKT activity.

CONCLUSIONS: We have demonstrated molecules that partially restore the GTPase activity of RASG12V. Their mechanism of action is well explained based on current RAS mutant conformation and mechanistic models. These molecules inhibit the RAS-AKT pathway and show higher cytotoxicity against cancer cells with the RASG12V mutation (SW-480 cell line). However, SW-480 cells can switch into the subline proliferating independently of AKT phosphorylation and show partial resistance to the molecules described in this article.

PMID:39891136 | DOI:10.1186/s12885-025-13580-8

Sci Rep. 2025 Jan 31;15(1):3946. doi: 10.1038/s41598-025-87867-y.

ABSTRACT

This paper focuses on the elective surgical scheduling problem with multi-resource constraints, including material resources, such as operating rooms (ORs) and non-operating room (NOR) beds, and human resources (i.e., surgeons, anesthesiologists, and nurses). The objective of multi-resource constrained elective surgical scheduling (MESS) is to simultaneously minimize the average recovery completion time for all patients, the average overtime for medical staffs, and the total medical cost. This problem can be formulated as a mixed integer linear multi-objective optimization model, and the honey badger algorithm based on the Nash equilibrium (HBA-NE) is developed for the MESS. Experimental studies were carried out to test the performance of the proposed approach, and the performance of the proposed surgical scheduling scheme was validated. Finally, to narrow the gap between the optimal surgical scheduling solution and actual hospital operations, digital twin (DT) technology is adopted to build a physical-virtual hospital surgery simulation model. The experimental results show that by introducing a digital twin, the physical and virtual spaces of the smart hospital can be integrated to visually simulate and verify surgical processes.

PMID:39890977 | DOI:10.1038/s41598-025-87867-y

Inflamm Res. 2025 Jan 31;74(1):31. doi: 10.1007/s00011-025-01996-8.

ABSTRACT

OBJECTIVE AND DESIGN: Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of inflammatory muscle disorders of unknown etiology. It is postulated that mitochondrial dysfunction and protein aggregation in skeletal muscle contribute to myofiber degeneration. However, molecular pathways that lead to protein aggregation in skeletal muscle are not well defined.

SUBJECTS: Here we have isolated membrane-bound organelles (e.g., nuclei, mitochondria, sarcoplasmic/endoplasmic reticulum, Golgi apparatus, and plasma membrane) from muscle biopsies of normal (n = 3) and muscle disease patients (n = 11). Of the myopathy group, 10 patients displayed mitochondrial abnormalities (IIM (n = 9); mitochondrial myopathy (n = 1)), and one IIM patient did not show mitochondrial abnormalities (polymyositis).

METHODS: Global proteomic analysis was performed using an Orbitrap Fusion mass spectrometer. Upon unsupervised clustering, normal and mitochondrial myopathy muscle samples clustered separately from IIM samples.

RESULTS: We have confirmed previously known protein alterations in IIM and identified several new ones. For example, we found differential expression of (i) nuclear proteins that control cell division, transcription, RNA regulation, and stability, (ii) ER and Golgi proteins involved in protein folding, degradation, and protein trafficking in the cytosol, and (iii) mitochondrial proteins involved in energy production/metabolism and alterations in cytoskeletal and contractile machinery of the muscle.

CONCLUSIONS: Our data demonstrates that molecular alterations are not limited to protein aggregations in the cytosol (inclusions) and occur in nuclear, mitochondrial, and membrane compartments of IIM skeletal muscle.

PMID:39890639 | DOI:10.1007/s00011-025-01996-8

FEMS Microbiol Lett. 2025 Jan 31:fnaf017. doi: 10.1093/femsle/fnaf017. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa has increasing clinical relevance and commonly occupies the cystic fibrosis (CF) airways. Its ability to colonize and persist in diverse niches is attributed to its large accessory genome, where prophages represent a common feature and may contribute to its fitness and persistence. We focused on the CF airways niche and used 197 longitudinal isolates from 12 patients persistently infected by P. aeruginosa. We computationally predicted intact prophages for each longitudinal group and scored their long-term persistence. We then confirmed prophage inducibility and mapped their location in the host chromosome with lysate sequencing. Using comparative genomics, we evaluated prophage genomic diversity, long-term persistence and level of genomic maintenance. Our findings support previous findings that most P. aeruginosa genomes harbour prophages some of which can self-induce, and that a common CF-treating antibiotic, ciprofloxacin, can induce prophages. Induced prophage genomes displayed high diversity and even genomic novelty. Finally, all induced prophages persisted long-term with their genomes avoiding gene loss and degradation over four years of host replication in the stressful CF airways niche. This and our detection of phage genes which contribute to host competitiveness and adaptation, lends support to our hypothesis that the vast majority of prophages detected as intact and inducible in this study facilitated their host fitness and persistence.

PMID:39890605 | DOI:10.1093/femsle/fnaf017

J Dairy Sci. 2025 Jan 29:S0022-0302(25)00046-3. doi: 10.3168/jds.2024-25812. Online ahead of print.

ABSTRACT

Increasing production and environmental challenges in dairy cattle means that selecting for resilience is becoming more important. This study explored whether data on cows that exit before completing their lactation and those that die during lactation can be used to predict resilience. To identify predictors of resilience, exiting the herd by 60, 120, 180, and 240 d were defined as traits. Additional traits were defined, by including all the cows that died during the entire lactation to the cows that exited at different times up to 240 d of lactation. For all traits, cows that exited the herd or died were coded as 1, otherwise as 0 at the end of the lactation. We used performance and exit data of Holstein (H) and Jersey (J) cows that calved between 1998 and 2023. The data were analyzed using a multi-trait sire model to estimate heritability and correlations with milk yield (MY), somatic cell count (SCC), calving interval (CIN), and selected type traits. The results showed that the proportion of cows that exited by 60 d was 2%, increasing by about 2% every 2 mo until exit by 240 d. The trend over the years in the proportion of exits, taking Exit 180d and Exit 180 + death as an example, showed an undesirable increase from 5.6% in 2000 to 9.4% in 2022. Heritability of all exit traits was low, increasing from below 1% for exit by 60 to 2.8% for exit by 240 d + all deaths over the lactation. The genetic correlation of early exit (i.e., 60 or 120 d) with first test-day MY was positive (unfavorable) and higher at the beginning (0.4), decreasing over time to be favorable in J (-0.2) and near zero in H (0.1) by the end of the lactation. On the other hand, the genetic correlation of exit with first test-day SCC became stronger (favorable) at the end of the lactation (0.3 to 0.4). Exit at any time during the lactation had the strongest genetic correlation with CIN (i.e., fertility). The genetic correlation of exit traits with body condition score (BCS) and angularity showed that the likelihood of cow exit, especially up to 180 d, was higher for thin and more angular cows. The genetic correlation estimates imply that cows with high potential for MY, poor fertility, poor BCS, and high scores for angularity are more likely to exit early due to metabolic stress. The change in genetic correlation between exit and MY early from unfavorable to favorable in J is due to more culling for milk and less for fertility and udder health is leading to an undesirable genetic trend for exit by 180 d as well as exit by 180 d + all death. However, the increasing phenotypic trend of exit rates in both breeds suggests a need for close monitoring. The selective use of exit data can help to develop genetic evaluations for resilience and health traits and validate and complement data collected to improve health and welfare during the transition period.

PMID:39890077 | DOI:10.3168/jds.2024-25812

Cell Rep Med. 2025 Jan 28:101931. doi: 10.1016/j.xcrm.2025.101931. Online ahead of print.

ABSTRACT

Chimeric antigen receptor (CAR) T cell therapy is a promising immunotherapy against cancer. Although there is a growing interest in other cell types, a comparison of CAR immune effector cells in challenging solid tumor contexts is lacking. Here, we compare mouse and human NKG2D-CAR-expressing T cells, natural killer (NK) cells, and macrophages against glioblastoma, the most aggressive primary brain tumor. Invitro we show that T cell cancer killing is CAR dependent, whereas intrinsic cytotoxicity overrules CAR dependence for NK cells, and CAR macrophages reduce glioma cells in co-culture assays. In orthotopic immunocompetent glioma mouse models, systemically administered CAR T cells demonstrate superior accumulation in the tumor, and each immune cell type induces distinct changes in the tumor microenvironment. An otherwise low therapeutic efficacy is significantly enhanced by co-expression of pro-inflammatory cytokines in all CAR immune effector cells, underscoring the necessity for multifaceted cell engineering strategies to overcome the immunosuppressive solid tumor microenvironment.

PMID:39889712 | DOI:10.1016/j.xcrm.2025.101931

Cell Rep Med. 2025 Jan 27:101935. doi: 10.1016/j.xcrm.2025.101935. Online ahead of print.

ABSTRACT

Chronic hepatic injury and inflammation from various causes can lead to fibrosis and cirrhosis, potentially predisposing to hepatocellular carcinoma. The molecular mechanisms underlying fibrosis and its progression remain incompletely understood. Using a proteo-transcriptomics approach, we analyze liver and plasma samples from 330 individuals, including 40 healthy individuals and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic dysfunction-associated steatotic liver disease. Our findings reveal dysregulated pathways related to extracellular matrix, immune response, inflammation, and metabolism in advanced fibrosis. We also identify 132 circulating proteins associated with advanced fibrosis, with neurofascin and growth differentiation factor 15 demonstrating superior predictive performance for advanced fibrosis(area under the receiver operating characteristic curve [AUROC] 0.89 [95% confidence interval (CI) 0.81-0.97]) compared to the fibrosis-4 model (AUROC 0.85 [95% CI 0.78-0.93]). These findings provide insights into fibrosis pathogenesis and highlight the potential for more accurate non-invasive diagnosis.

PMID:39889710 | DOI:10.1016/j.xcrm.2025.101935

Semin Cell Dev Biol. 2025 Jan 30;167:22-39. doi: 10.1016/j.semcdb.2025.01.002. Online ahead of print.

ABSTRACT

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

PMID:39889540 | DOI:10.1016/j.semcdb.2025.01.002

Phytomedicine. 2025 Jan 20;138:156412. doi: 10.1016/j.phymed.2025.156412. Online ahead of print.

ABSTRACT

BACKGROUND: Vascular dementia (VaD), a significant cognitive disorder, is caused by reduced cerebral blood flow. Unraveling the metabolic heterogeneity and reprogramming in VaD is essential for understanding its molecular pathology and developing targeted therapies. However, the in situ metabolic regulation within the specific brain regions affected by VaD has not been thoroughly investigated, and the therapeutic mechanisms of Erigeron breviscapus injection (EBI), a traditional Chinese medicine, require further elucidation.

PURPOSE: To investigate the region-specific metabolic alterations in a VaD rat model, explore the therapeutic effects of EBI at a microregional level, identify the key metabolic pathways and metabolites involved in VaD, and elucidate how EBI modulates these pathways to exert its therapeutic effects.

METHODS: Air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), a novel technique, was employed to investigate the metabolic changes in the brain microregions. We used a bilateral common carotid artery occlusion model to induce VaD in rats. Network analysis and network pharmacology were used to assess the local metabolic effects of the EBI treatment (3.6 mL/kg/day for 2 weeks).

RESULTS: The EBI treatment significantly ameliorated the neurological deficits in VaD rats. AFADESI-MSI revealed 31 key metabolites with significant alterations in the VaD model, particularly within the pathways related to neurotransmitter metabolism, redox homeostasis, and osmoregulation. The metabolic disturbances were primarily observed in the striatum (ST), pyriform cortex (PCT), hippocampus (HP), and other critical brain regions. The EBI treatment effectively reversed these metabolic imbalances, especially in neurotransmitter metabolism, suggesting its potential in mitigating VaD-related cognitive decline.

CONCLUSION: Our findings not only shed light on the molecular underpinnings of VaD but also highlight the potential of EBI as a therapeutic agent in neurodegenerative disorders. Moreover, this study demonstrates the power of advanced mass spectrometry imaging techniques in phytomedicine, offering new insights into the spatial metabolic changes induced by botanical treatments.

PMID:39889490 | DOI:10.1016/j.phymed.2025.156412

J Neural Eng. 2025 Jan 31. doi: 10.1088/1741-2552/adb0f2. Online ahead of print.

ABSTRACT

Abstract-Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio (SNR) and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.

APPROACH: This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning. Specifically, this study enhanced SSVEP features using Global template alignment (GTA) and Discriminant Spatial Pattern (DSP), and then designed a Compacted Temporal-Spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.

MAIN RESULTS: The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, deep learning methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset.The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.

SIGNIFICANCE: This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.

PMID:39889306 | DOI:10.1088/1741-2552/adb0f2

J Opt Soc Am A Opt Image Sci Vis. 2024 Jul 1;41(7):1426-1434. doi: 10.1364/JOSAA.523092.

ABSTRACT

Coded aperture compressive temporal imaging (CACTI) is a recently developed imaging technique based on the theory of compressed sensing. It uses an optical imaging system to sample a high-speed dynamic scene (a set of consecutive video frames), integrates the sampled data in time according to masks (sensing matrix), and thus obtains compressive measurements. Considerable effort has been devoted to the sampling strategy and the ill-posed inverse process of reconstructing a three-dimensional (3D) high-speed dynamic scene from two-dimensional (2D) compressive measurements. The importance of the reconstruction algorithm and the optimization mask is evident. In this paper, a flexible, efficient, and superior quality Landweber iterative method is proposed for video reconstruction through jointly learning the optimal binary mask strategy, relaxation strategy, and regularization strategy. To solve the sparse representation problem in iteration, multiple denoisers are introduced to obtain more regularization prior information. By combining the mathematical structure of the Landweber iterative reconstruction method with deep learning, the challenging parameter selection procedure is successfully tackled. Extensive experimental results demonstrate the superiority of the proposed method.

PMID:39889132 | DOI:10.1364/JOSAA.523092

J Opt Soc Am A Opt Image Sci Vis. 2024 Nov 1;41(11):2263-2276. doi: 10.1364/JOSAA.533551.

ABSTRACT

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

PMID:39889089 | DOI:10.1364/JOSAA.533551

J Opt Soc Am A Opt Image Sci Vis. 2024 Sep 1;41(9):1786-1793. doi: 10.1364/JOSAA.526142.

ABSTRACT

Disorders affecting the retina pose a considerable risk to human vision, with an array of factors including aging, diabetes, hypertension, obesity, ocular trauma, and tobacco use exacerbating this issue in contemporary times. Optical coherence tomography (OCT) is a rapidly developing imaging modality that is capable of identifying early signs of vascular, ocular, and central nervous system abnormalities. OCT can diagnose retinal diseases through image classification, but quantifying the laceration area requires image segmentation. To overcome this obstacle, we have developed an innovative deep learning framework that can perform both tasks simultaneously. The suggested framework employs a parallel mask-guided convolutional neural network (PM-CNN) for the classification of OCT B-scans and a grade activation map (GAM) output from the PM-CNN to help a V-Net network (GAM V-Net) to segment retinal lacerations. The guiding mask for the PM-CNN is obtained from the auxiliary segmentation job. The effectiveness of the dual framework was evaluated using a combined dataset that encompassed four publicly accessible datasets along with an additional real-time dataset. This compilation included 11 categories of retinal diseases. The four publicly available datasets provided a robust foundation for the validation of the dual framework, while the real-time dataset enabled the framework's performance to be assessed on a broader range of retinal disease categories. The segmentation Dice coefficient was 78.33±0.15%, while the classification accuracy was 99.10±0.10%. The model's ability to effectively segment retinal fluids and identify retinal lacerations on a different dataset was an excellent demonstration of its generalizability.

PMID:39889044 | DOI:10.1364/JOSAA.526142

J Opt Soc Am A Opt Image Sci Vis. 2024 Sep 1;41(9):1702-1712. doi: 10.1364/JOSAA.529243.

ABSTRACT

Phase retrieval is about reconstructing original vectors/images from their Fourier intensity measurements. Deep learning methods have been introduced to solve the phase retrieval problem; however, most of the proposed approaches cannot improve the reconstruction quality of phase and amplitude of original images simultaneously. In this paper, we present a distributed amplitude and phase conditional generative adversarial network (D-APUCGAN) to achieve the high quality of phase and amplitude images at the same time. D-APUCGAN includes UCGAN, AUCGAN/PUCGAN, and APUCGAN. In this paper, we introduce the content loss function to constrain the similarity between the reconstructed image and the source image through the Frobenius norm and the total variation modulus. The proposed method promotes the quality of phase images better than just using amplitude images to train. The numerical experimental results show that the proposed cascade strategies are significantly effective and remarkable for natural and unnatural images, DIV2K testing datasets, MNIST dataset, and realistic data. Comparing with the conventional neural network methods, the evaluation metrics of PSNR and SSIM values in the proposed method are refined by about 2.25 dB and 0.18 at least, respectively.

PMID:39889034 | DOI:10.1364/JOSAA.529243

J Opt Soc Am A Opt Image Sci Vis. 2024 Oct 1;41(10):1987-1993. doi: 10.1364/JOSAA.531198.

ABSTRACT

The $\pm 1$st order diffraction of gratings is widely used in spectral analysis. However, when the incident light is non-monochromatic, the higher-order diffractions generated by traditional diffraction gratings are always superimposed on the useful first-order diffraction, complicating subsequent spectral decoding. In this paper, single-order diffraction gratings with a sinusoidal transmittance, called hexagonal diffraction gratings (HDGs), are designed using a convolutional neural network based on deep learning algorithm. The trained convolutional neural network can accurately retrieve the structural parameters of the HDGs. Simulation and experimental results confirm that the HDGs can effectively suppress higher-order diffractions above the third order. The intensity of third-order diffraction is reduced from 20% of the first-order diffraction to less than that of the background. This higher-order diffraction suppression property of the HDGs is promising for applications in fields such as synchrotron radiation, astrophysics, and soft x-ray lasers.

PMID:39889023 | DOI:10.1364/JOSAA.531198

J Opt Soc Am A Opt Image Sci Vis. 2024 Oct 1;41(10):1979-1986. doi: 10.1364/JOSAA.525577.

ABSTRACT

The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing images in a memory-efficient way. Thus, in this work, we have designed a deep-learning-based CIR technique that selects key pixels using the guided U-Net (GU-Net) architecture [Asian Conference on Pattern Recognition, p. 317 (2023)], and then near-original images are constructed using a conditional generative adversarial network (GAN)-based architecture. The technique was evaluated on two microscopy- and two scanner-captured-image datasets and obtained good performance in terms of storage requirements and quality of the reconstructed images.

PMID:39889022 | DOI:10.1364/JOSAA.525577

Sci Adv. 2025 Jan 31;11(5):eads4224. doi: 10.1126/sciadv.ads4224. Epub 2025 Jan 31.

ABSTRACT

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. Here, we introduce a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. We fabricate NOMA with more than half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules per pixel. With NOMA, we demonstrate an optical multilayer neural network. Our work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

PMID:39888986 | DOI:10.1126/sciadv.ads4224

PLoS One. 2025 Jan 31;20(1):e0317990. doi: 10.1371/journal.pone.0317990. eCollection 2025.

ABSTRACT

The continuous development of the field of artificial intelligence, not only makes people's lives more convenient but also plays a role in the supervision and protection of people's lives and property safety. News of the fire is not uncommon, and fire has become the biggest hidden danger threatening the safety of public life and property. In this paper, a deep learning-based flame recognition method for complex scenes, GGSYOLOv5, is proposed. Firstly, a global attention mechanism (GAM) was added to the CSP1 module in the backbone part of the YOLOv5 network, and then a parameterless attention mechanism was added to the feature fusion part. Finally, packet random convolution (GSConv) was used to replace the original convolution at the output end. A large number of experiments show that the detection accuracy rate is 4.46% higher than the original algorithm, and the FPS is as high as 64.3, which can meet the real-time requirements. Moreover, the algorithm is deployed in the Jetson Nano embedded development board to build the flame detection system.

PMID:39888970 | DOI:10.1371/journal.pone.0317990

PLoS One. 2025 Jan 31;20(1):e0317996. doi: 10.1371/journal.pone.0317996. eCollection 2025.

ABSTRACT

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sections individually under a microscope, which is a time-consuming process with low interobserver consistency. With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. Thus, in this study, we developed a model for intraoperative cytological segmentation of pulmonary lesions based on the YOLOv8 algorithm, which labels each instance by segmenting the image at the pixel level. The model achieved a mean pixel accuracy and mean intersection over union of 0.80 and 0.70, respectively, on the test set. At the image level, the accuracy and area under the receiver operating characteristic curve values for malignant and benign (or normal) lesions were 91.0% and 0.90, respectively. In addition, the model was deemed suitable for diagnosing pleural fluid cytology and bronchoalveolar lavage fluid cytology images. The model predictions were strongly correlated with pathologist diagnoses and the gold standard, indicating the model's ability to make clinical-level decisions during initial diagnosis. Thus, the proposed method is useful for rapidly localizing lung cancer cells based on microscopic images and outputting image interpretation results.

PMID:39888907 | DOI:10.1371/journal.pone.0317996

Medicine (Baltimore). 2025 Jan 31;104(5):e41460. doi: 10.1097/MD.0000000000041460.

ABSTRACT

The present study aims to evaluate the adverse events associated with Capmatinib using real-world data, providing a reference basis for its rational use in clinical practice. Relevant data from the Food and Drug Administration adverse event reporting system database was mined. Next, reporting odds ratio and Bayesian confidence propagation neural network method were used to analyze real-world adverse events associated with Capmatinib. The study revealed significant adverse event signals of Capmatinib, primarily involving general disorders and administration site conditions, cardiac disorders, gastrointestinal disorders, respiratory, thoracic and mediastinal disorders, neoplasms benign, malignant and unspecified (including cysts and polyps) and investigations, among others. A total of 79 signals were identified, with 13 of them not mentioned in the drug's specifications. Taken together, our comprehensive analysis of the Food and Drug Administration adverse event reporting system database enhances the understanding of Capmatinib's safety profile, thereby contributing to informed decision-making in its clinical application and facilitating the timely management of associated adverse reactions.

PMID:39889151 | DOI:10.1097/MD.0000000000041460