Microb Genom. 2025 Jan;11(1). doi: 10.1099/mgen.0.001332.

ABSTRACT

Bacteria from the Stenotrophomonas maltophilia complex (Smc) are important multidrug-resistant pathogens that cause a broad range of infections. Smc is genomically diverse and has been classified into 23 lineages. Lineage Sm6 is the most common among sequenced strains, but it is unclear why this lineage has evolved to be dominant. Antagonistic interactions can significantly affect the evolution of bacterial populations. These interactions may be mediated by secreted contact-dependent proteins, which allow inhibitor cells to intoxicate adjacent target bacteria. Contact-dependent inhibition (CDI) requires three proteins: CdiA, CdiB and CdiI. CdiA is a large, filamentous protein exported to the surface of inhibitor cells through the pore-like CdiB. The CdiA C-terminal domain (CdiA-CT) is toxic when delivered into target cells of the same species or genus. CdiI immunity proteins neutralize the toxicity of cognate CdiA-CT toxins. We found that all complete Smc genomes from the Sm6 lineage harbour at least one CDI locus. By contrast, less than a quarter of strains from other lineages have CDI genes. Smc CdiA-CT domains are diverse and have a broad range of predicted functions. Most Sm6 strains harbour non-cognate cdiI genes predicted to provide protection against foreign toxins from other strains. Finally, we demonstrated that an Smc CdiA-CT toxin has antibacterial properties and is neutralized by its cognate CdiI.

PMID:39853206 | DOI:10.1099/mgen.0.001332

Biomed Phys Eng Express. 2025 Jan 24. doi: 10.1088/2057-1976/adae14. Online ahead of print.

ABSTRACT

Traumatic injury remains a leading cause of death worldwide, with traumatic bleeding being one of its most critical and fatal consequences. The use of whole-body computed tomography (WBCT) in trauma management has rapidly expanded. However, interpreting WBCT images within the limited time available before treatment is particularly challenging for acute care physicians. Our group has previously developed an automated bleeding detection method in WBCT images. However, further reduction of false positives (FPs) is necessary for clinical application. To address this issue, we propose a novel automated detection for traumatic bleeding in CT images using deep learning and multi-organ segmentation; Methods: The proposed method integrates a three-dimensional U-Net# model for bleeding detection with an FP reduction approach based on multi-organ segmentation. The multi-organ segmentation method targets the bone, kidney, and vascular regions, where FPs are primarily found during the bleeding detection process. We evaluated the proposed method using a dataset of delayed-phase contrast-enhanced trauma CT images collected from four institutions; Results: Our method detected 70.0% of bleedings with 76.2 FPs/case. The processing time for our method was 6.3 ± 1.4 min. Compared with our previous ap-proach, the proposed method significantly reduced the number of FPs while maintaining detection sensitivity.

PMID:39854772 | DOI:10.1088/2057-1976/adae14

PLoS One. 2025 Jan 24;20(1):e0312177. doi: 10.1371/journal.pone.0312177. eCollection 2025.

ABSTRACT

In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention on another side. It is occasionally desirable to analyze such orientation estimates using computer-vision tools for automated analysis of pedestrian behavior and intention. This article uses a deep-learning method to demonstrate the pedestrian full-body pose estimation approach. A deep-learning-based pre-trained supervised model multi-branched deep learning pose net (MBDLP-Net) is proposed for estimation and classification. For full-body pose and orientation estimation, three independent datasets, an extensive dataset for body orientation (BDBO), PKU-Reid, and TUD Multiview Pedestrians, are used. Independently, the proposed technique is trained on dataset CIFAR-100 with 100 classes. The proposed approach is meticulously tested using publicly accessible BDBO, PKU-Reid, and TUD datasets. The results show that the mean accuracy for full-body pose estimation with BDBO and PKU-Reid is 0.95%, and with TUD multiview pedestrians is 0.97%. The performance results show that the proposed technique efficiently distinguishes full-body poses and orientations in various configurations. The efficacy of the provided approach is compared with existing pretrained, robust, and state-of-the-art methodologies, providing a comprehensive understanding of its advantages.

PMID:39854382 | DOI:10.1371/journal.pone.0312177

PLoS One. 2025 Jan 24;20(1):e0312363. doi: 10.1371/journal.pone.0312363. eCollection 2025.

ABSTRACT

The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected. After preprocessing, a total of 20,152 valid images were obtained for the experimental samples. The network then extracts both shallow and deep features from these maize images, which are subsequently fused. Concurrently, the extracted features undergo further processing through a specially designed convolutional block. The fused features, combined with those processed by the convolutional module, are fed into an attention layer. This attention layer assigns weights to the features, facilitating accurate final classification. Experimental results demonstrate that the MC-Swin Transformer model proposed in this paper significantly outperforms traditional convolutional neural network models in key metrics such as accuracy, precision, recall, and F1 score, achieving a recognition accuracy rate of 99.89%. Thus, the network effectively and efficiently classifies different corn qualities. This study not only offers a novel perspective and technical approach to corn quality detection but also holds significant implications for the advancement of smart agriculture.

PMID:39854315 | DOI:10.1371/journal.pone.0312363

Bioinformatics. 2025 Jan 24:btaf035. doi: 10.1093/bioinformatics/btaf035. Online ahead of print.

ABSTRACT

MOTIVATION: In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive. As we are not aware of any methods that can identify new terms in functional ontologies, we ask if they can, at least, identify molecular functions of proteins that are non-homologous to or far-removed from known protein families.

RESULTS: Here, we explore the potential and limitations of the existing methods in predicting molecular functions of thousands of such proteins. Lacking the "ground truth" functional annotations, we transformed the assessment of function prediction into evaluation of functional similarity of protein pairs that likely share function but are unlike any of the currently functionally annotated sequences. Notably, our approach transcends the limitations of functional annotation vocabularies, providing a means to assess different-ontology annotation methods. We find that most existing methods are limited to identifying functional similarity of homologous sequences and fail to predict function of proteins lacking reference. Curiously, despite their seemingly unlimited by-homology scope, deep learning methods also have trouble capturing the functional signal encoded in protein sequence. We believe that our work will inspire the development of a new generation of methods that push boundaries and promote exploration and discovery in the molecular function domain.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39854283 | DOI:10.1093/bioinformatics/btaf035

Transl Vis Sci Technol. 2025 Jan 2;14(1):27. doi: 10.1167/tvst.14.1.27.

ABSTRACT

PURPOSE: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.

METHODS: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs). Key developments and practical applications of these models in OCT image analysis were emphasized, particularly in the context of enhancing image quality, glaucoma diagnosis, and monitoring progression.

RESULTS: CNNs excel in segmenting retinal layers and detecting glaucomatous damage, whereas RNNs are effective in analyzing sequential OCT scans for disease progression. GANs enhance image quality and data augmentation, and autoencoders facilitate advanced feature extraction. LLMs show promise in integrating textual and visual data for comprehensive diagnostic assessments. Despite these advancements, challenges such as data availability, variability, potential biases, and the need for extensive validation persist.

CONCLUSIONS: DL models are reshaping glaucoma management by enhancing OCT's diagnostic capabilities. However, the successful translation into clinical practice requires addressing major challenges related to data variability, biases, fairness, and model validation to ensure accurate and reliable patient care.

TRANSLATIONAL RELEVANCE: This review bridges the gap between basic research and clinical care by demonstrating how AI, particularly DL models, can markedly enhance OCT's clinical utility in diagnosis, monitoring, and prediction, moving toward more individualized, personalized, and precise treatment strategies.

PMID:39854198 | DOI:10.1167/tvst.14.1.27

J Eur Acad Dermatol Venereol. 2025 Jan 24. doi: 10.1111/jdv.20538. Online ahead of print.

ABSTRACT

BACKGROUND: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).

METHODS: We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient. The model was trained and validated on a discovery cohort of primary cutaneous melanomas (IHP-MEL-1, n = 342). Performance was tested on two external and independent datasets (IHP-MEL-2, n = 161; and TCGA cohort n = 63). It was compared with well-established prognostic factors. Concordance index (c-index) was used as a metric.

RESULTS: On the discovery cohort, the SmartProg-MEL predicts the 5-y OS with a c-index of 0.78 on the cross-validation data and of 0.72 on the cross-testing series. In the external cohorts, the model achieved a c-index of 0.71 and 0.69 for the IHP-MEL-2 and TCGA dataset respectively. Furthermore, SmartProg-MEL was an independent and the most powerful prognostic factor in multivariate analysis (HR = 1.84, p-value < 0.005). Finally, the model was able to dichotomize patients in two groups-a low and a high-risk group-each associated with a significantly different 5-y OS (p-value < 0.001 for IHP-MEL-1 and p-value = 0.01 for IHP-MEL-2).

CONCLUSION: The performance of our fully automated SmartProg-MEL model outperforms the current clinicopathological factors in terms of prediction of 5-y OS and risk stratification of cutaneous melanoma patients. Incorporation of SmartProg-MEL in the clinical workflow could guide the decision-making process by improving the identification of patients that may benefit from adjuvant therapy.

PMID:39853986 | DOI:10.1111/jdv.20538

CNS Neurosci Ther. 2025 Jan;31(1):e70235. doi: 10.1111/cns.70235.

ABSTRACT

AIMS: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.

MATERIALS: We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH. Additionally, 26 SECT scans were included as an external validation set. We compared our trans-GAN with state-of-the-art generation methods using several physical metrics of the generated images and evaluated the diagnostic efficacy of the generated images for differentiating ICH from contrast staining.

RESULTS: In comparison with other generation methods, the images generated by trans-GAN exhibited superior quantitative performance. Meanwhile, in terms of ICH detection, the use of generated images from both the internal and external validation sets resulted in a higher area under the receiver operating characteristic curve (0.88 vs. 0.68 and 0.69 vs. 0.54, respectively) and kappa values (0.83 vs. 0.56 and 0.51 vs. 0.31, respectively) compared with input SECT images.

CONCLUSION: Our proposed trans-GAN provides a new approach based on SECT for real-time differentiation of ICH and contrast staining in hospitals without DECT conditions.

PMID:39853936 | DOI:10.1111/cns.70235

Neurosurg Rev. 2025 Jan 24;48(1):79. doi: 10.1007/s10143-025-03230-9.

ABSTRACT

Postoperative facial nerve (FN) dysfunction is associated with a significant impact on the quality of life of patients and can result in psychological stress and disorders such as depression and social isolation. Preoperative prediction of FN outcomes can play a critical role in vestibular schwannomas (VSs) patient care. Several studies have developed machine learning (ML)-based models in predicting FN outcomes following resection of VS. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of ML-based models in predicting FN outcomes following resection in the setting of VS. On December 12, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated the performance outcomes of the ML-based predictive models were included. The pooled sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated through the R program. Five studies with 807 individuals with VS, encompassing 35 models, were included. The meta-analysis showed a pooled sensitivity of 82% (95%CI: 76-87%), specificity of 79% (95%CI: 74-84%), and DOR of 12.94 (95%CI: 8.65-19.34) with an AUC of 0.841. The meta-analysis of the best performance model demonstrated a pooled sensitivity of 91% (95%CI: 80-96%), specificity of 87% (95%CI: 82-91%), and DOR of 46.84 (95%CI: 19.8-110.8). Additionally, the analysis demonstrated an AUC of 0.92, a sensitivity of 0.884, and a false positive rate of 0.136 for the best performance models. ML-based models possess promising diagnostic accuracy in predicting FN outcomes following resection.

PMID:39853510 | DOI:10.1007/s10143-025-03230-9

Pediatr Radiol. 2025 Jan 24. doi: 10.1007/s00247-025-06165-x. Online ahead of print.

ABSTRACT

BACKGROUND: Motion correction methods based on slice-to-volume registration (SVR) for fetal magnetic resonance imaging (MRI) allow reconstruction of three-dimensional (3-D) isotropic images of the fetal brain and body. However, all existing SVR methods are confined to research settings, which limits clinical integration. Furthermore, there have been no reported SVR solutions for low-field 0.55-T MRI.

OBJECTIVE: Integration of automated SVR motion correction methods directly into fetal MRI scanning process via the Gadgetron framework to enable automated T2-weighted (T2W) 3-D fetal brain and body reconstruction in the low-field 0.55-T MRI scanner within the duration of the scan.

MATERIALS AND METHODS: A deep learning fully automated pipeline was developed for T2W 3-D rigid and deformable (D/SVR) reconstruction of the fetal brain and body of 0.55-T T2W datasets. Next, it was integrated into 0.55-T low-field MRI scanner environment via a Gadgetron workflow that enables launching of the reconstruction process directly during scanning in real-time.

RESULTS: During prospective testing on 12 cases (22-40 weeks gestational age), the fetal brain and body reconstructions were available on average 6:42 ± 3:13 min after the acquisition of the final stack and could be assessed and archived on the scanner console during the ongoing fetal MRI scan. The output image data quality was rated as good to acceptable for interpretation. The retrospective testing of the pipeline on 83 0.55-T datasets demonstrated stable reconstruction quality for low-field MRI.

CONCLUSION: The proposed pipeline allows scanner-based prospective T2W 3-D motion correction for low-field 0.55-T fetal MRI via direct online integration into the scanner environment.

PMID:39853394 | DOI:10.1007/s00247-025-06165-x

Anal Chem. 2025 Jan 24. doi: 10.1021/acs.analchem.4c05877. Online ahead of print.

ABSTRACT

Digital fluorescence immunoassay (DFI) based on random dispersion magnetic beads (MBs) is one of the powerful methods for ultrasensitive determination of protein biomarkers. However, in the DFI, improving the limit of detection (LOD) is challenging since the ratio of signal-to-background and the speed of manual counting beads are low. Herein, we developed a deep-learning network (ATTBeadNet) by utilizing a new hybrid attention mechanism within a UNet3+ framework for accurately and fast counting the MBs and proposed a DFI using CdS quantum dots (QDs) with narrow peak and optical stability as reported at first time. The developed ATTBeadNet was applied to counting the MBs, resulting in the F1 score (95.91%) being higher than those of other methods (ImageJ, 68.33%; computer vision-based, 92.99%; fully convolutional network, 75.00%; mask region-based convolutional neural network, 70.34%). On principle-on-proof, a sandwich MB-based DFI was proposed, in which human interleukin-6 (IL-6) was taken as a model protein biomarker, while antibody-bound streptavidin-coated MBs were used as capture MBs and antibody-HRP-tyramide-functionalized CdS QDs were used as the binding reporter. When the developed ATTBeadNet was applied to the MB-based DFI of IL-6 (20 μL), the linear range from 5 to 100 fM and an LOD of 3.1 fM were achieved, which are better than those using the ImageJ method (linear range from 30 to 100 fM and LOD of 20 fM). This work demonstrates that the integration of the deep-learning network with DFI is a promising strategy for the highly sensitive and accurate determination of protein biomarkers.

PMID:39853309 | DOI:10.1021/acs.analchem.4c05877

Medicine (Baltimore). 2025 Jan 24;104(4):e41320. doi: 10.1097/MD.0000000000041320.

ABSTRACT

While recent studies suggested a potential causal link between type 1 diabetes mellitus (T1DM) but not type 2 diabetes mellitus (T2DM) and idiopathic pulmonary fibrosis (IPF), the involved mechanism remains unclear. Here, using a Mendelian randomization (MR) approach, we verified the causal relationship between the two types of diabetes mellitus and IPF and investigated the possible role of inflammation in the association between diabetes mellitus and IPF. Based on genome-wide association study (GWAS) summary data of T1DM, T2DM, and IPF, the univariable MR, multivariable MR (MVMR), and mediation MR were successively used to analyze the causal relationship. Inverse variance weighted was used as the main method to infer the causal effect, together with a series of sensitivity analyses. The univariable MR showed that only T1DM increased the risk of IPF, and there was no significant causal relationship between T2DM and IPF. The MVMR further verified that there was an independent direct causal effect of T1DM on IPF. Further mediation analysis showed that this effect was partly mediated by increasing C-X-C motif chemokine ligand 10 (CXCL10) and interleukin-12 subunit beta (IL-12B). In conclusion, T1DM is related to an increased risk of IPF. Notably, the causal effect was partially mediated by CXCL10 and IL-12B. Hence, monitoring T1DM patients may help in the early detection and prevention of IPF.

PMID:39854757 | DOI:10.1097/MD.0000000000041320

Sports (Basel). 2025 Jan 8;13(1):9. doi: 10.3390/sports13010009.

ABSTRACT

Chronic lung diseases such as Chronic Obstructive Pulmonary Disease, Interstitial Lung Disease (ILD), and Pulmonary Hypertension (PH) are characterized by progressive symptoms such as dyspnea, fatigue, and muscle weakness, often leading to physical inactivity, and reduced quality of life. Many patients also experience significantly impaired exercise tolerance. While pulmonary, cardiovascular, respiratory, and peripheral muscle dysfunction contribute to exercise limitations, recent evidence suggests that hypoxia and impairments in cerebral oxygenation may also play a role in exercise intolerance. This narrative review (i) summarizes studies investigating cerebral oxygenation responses during exercise in patients with different types of chronic lung diseases and (ii) discusses possible mechanisms behind the blunted cerebral oxygenation during exercise reported in many of these conditions; however, the extent of cerebral desaturation and the intensity at which it occurs can vary. These differences depend on the specific pathophysiology of the lung disease and the presence of comorbidities. Notably, reduced cerebral oxygenation during exercise in fibrotic-ILD has been linked with the development of dyspnea and early exercise termination. Understanding the effects of chronic lung disease on cerebral oxygenation during exercise may improve our understanding of exercise intolerance mechanisms and help identify therapeutic strategies to enhance brain health and exercise capacity in these patients.

PMID:39852605 | DOI:10.3390/sports13010009

PLoS Biol. 2025 Jan 24;23(1):e3002722. doi: 10.1371/journal.pbio.3002722. eCollection 2025 Jan.

ABSTRACT

Successful resolution of approach-avoidance conflict (AAC) is fundamentally important for survival, and its dysregulation is a hallmark of many neuropsychiatric disorders, and yet the underlying neural circuit mechanisms are not well elucidated. Converging human and animal research has implicated the anterior/ventral hippocampus (vHPC) as a key node in arbitrating AAC in a region-specific manner. In this study, we sought to target the vHPC CA1 projection pathway to the nucleus accumbens (NAc) to delineate its contribution to AAC decision-making, particularly in the arbitration of learned reward and punishment signals, as well as innate signals. To this end, we used pathway-specific chemogenetics in male and female Long Evans rats to inhibit the NAc shell projecting vHPC CA1 neurons while rats underwent a test in which cues of positive and negative valence were presented concurrently to elicit AAC. Additional behavioral assays of social preference and memory, reward and punishment cue processing, anxiety, and novelty processing were administered to further interrogate the conditions under which the vCA1-NAc shell pathway is recruited. Chemogenetic inhibition of the vCA1-NAc shell circuit resulted in animals exhibiting increased decision-making time and avoidance bias specifically in the face of motivational conflict, as the same behavioral phenotype was absent in separate conditioned cue preference and avoidance tests. vCA1-NAc shell inhibition also led to a reduction in seeking social interaction with a novel rat but did not alter anxiety-like behaviors. The vCA1-NAc shell circuit is therefore critically engaged in biasing decisions to approach in the face of social novelty and approach-avoidance conflict. Dysregulation of this circuit could lead to the precipitation of addictive behaviors in substance abuse, or maladaptive avoidance in situations of approach-avoidance conflict.

PMID:39854559 | DOI:10.1371/journal.pbio.3002722

Sci Adv. 2025 Jan 24;11(4):eads1911. doi: 10.1126/sciadv.ads1911. Epub 2025 Jan 24.

ABSTRACT

Tigilanol tiglate (EBC-46) is a selective modulator of protein kinase C (PKC) isoforms that is Food and Drug Administration (FDA) approved for the treatment of mast cell tumors in canines with up to an 88% cure rate. Recently, it has been FDA approved for the treatment of soft tissue sarcomas in humans. The role of EBC-46 and, especially, its analogs in efforts to eradicate HIV, treat neurological and cardiovascular disorders, or enhance antigen density in antigen-targeted chimeric antigen receptor-T cell and chimeric antigen receptor-natural killer cell immunotherapies has not been reported. Enabled by our previously reported scalable synthesis of EBC-46, we report herein the systematic design, synthesis, and evaluation of EBC-46 analogs, including those inaccessible from the natural source and their PKC affinities, ability to translocate PKC, nuclear factor κB activity, and efficacy in reversing HIV latency in Jurkat-Latency cells. Leading analogs show exceptional PKC affinities, isoform selectivities, and functional activities, serving as promising candidates for therapeutic applications.

PMID:39854456 | DOI:10.1126/sciadv.ads1911

Proc Natl Acad Sci U S A. 2025 Jan 28;122(4):e2426163122. doi: 10.1073/pnas.2426163122. Epub 2025 Jan 24.

NO ABSTRACT

PMID:39854239 | DOI:10.1073/pnas.2426163122

Syst Biol Reprod Med. 2025 Dec;71(1):1. doi: 10.1080/19396368.2024.2447691. Epub 2025 Jan 24.

NO ABSTRACT

PMID:39854222 | DOI:10.1080/19396368.2024.2447691

Diabetes. 2025 Jan 24:db240497. doi: 10.2337/db24-0497. Online ahead of print.

ABSTRACT

PPARγ is the pharmacological target of thiazolidinediones (TZDs), potent insulin sensitizers that prevent metabolic disease morbidity but are accompanied by side effects such as weight gain, in part due to non-physiological transcriptional agonism. Using high throughput genome engineering, we targeted nonsense mutations to every exon of PPARG, finding an ATG in Exon 2 (chr3:12381414, CCDS2609 c.A403) that functions as an alternative translational start site. This downstream translation initiation site gives rise to a PPARγ protein isoform (M135), preferentially generated from alleles containing nonsense mutations upstream of c.A403. PPARγ M135 retains the DNA and ligand binding domains of full-length PPARγ but lacks the N-terminal AF-1 domain. Despite being truncated, PPARγ M135 shows increased transactivation of target genes, but only in the presence of agonists. Accordingly, human missense mutations disrupting AF-1 domain function actually increase agonist-induced cellular PPARγ activity compared to wild-type (WT), and carriers of these AF-1 disrupting variants are protected from metabolic syndrome. Thus, we propose the existence of PPARγ M135 as a fully functional, alternatively translated isoform that may be therapeutically generated to treat insulin resistance-related disorders.

PMID:39854214 | DOI:10.2337/db24-0497

STAR Protoc. 2025 Jan 23;6(1):103584. doi: 10.1016/j.xpro.2024.103584. Online ahead of print.

ABSTRACT

Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST- and corresponding DT-CPD data for specific protein pairs and evaluating the model's performance on hold-out test sets. For complete details on the use and execution of this protocol, please refer to Srinivasan and Bajorath.1.

PMID:39854202 | DOI:10.1016/j.xpro.2024.103584

Adv Sci (Weinh). 2025 Jan 24:e2413367. doi: 10.1002/advs.202413367. Online ahead of print.

ABSTRACT

Glioblastoma multiforme (GBM) is a highly aggressive and malignant brain tumor originating from glial cells, characterized by high recurrence rates and poor patient prognosis. The heterogeneity and complex biology of GBM, coupled with the protective nature of the blood-brain barrier (BBB), significantly limit the efficacy of traditional therapies. The rapid development of nanoenzyme technology presents a promising therapeutic paradigm for the rational and targeted treatment of GBM. In this review, the underlying mechanisms of GBM pathogenesis are comprehensively discussed, emphasizing the impact of the BBB on treatment strategies. Recent advances in nanoenzyme-based approaches for GBM therapy are explored, highlighting how these nanoenzymes enhance various treatment modalities through their multifunctional capabilities and potential for precise drug delivery. Finally, the challenges and therapeutic prospects of translating nanoenzymes from laboratory research to clinical application, including issues of stability, targeting efficiency, safety, and regulatory hurdles are critically analyzed. By providing a thorough understanding of both the opportunities and obstacles associated with nanoenzyme-based therapies, future research directions are aimed to be informed and contribute to the development of more effective treatments for GBM.

PMID:39854126 | DOI:10.1002/advs.202413367