Br J Radiol. 2025 Apr 9:tqaf077. doi: 10.1093/bjr/tqaf077. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction(DLIR) in coronary computed tomography angiography(CCTA) for slender patients with body-mass-index (BMI)≤25kg/m2.

METHODS: Sixty patients for CCTA were randomly divided into two groups: group A with 120 kV and contrast agent dose of 0.8 ml/kg, and group B with 70 kV and contrast agent dose of 0.5 ml/kg.Group A used adaptive statistical iterative reconstruction-V(ASIR-V) with 50% strength level(50%ASIR-V) while group B used 50%ASIR-V, DLIR of low level(DLIR-L),DLIR of medium level(DLIR-M) and DLIR of high level(DLIR-H) for image reconstruction. The CT values and SD values of coronary arteries and pericardial fat were measured, and signal-to-noise ratio(SNR) and contrast-to-noise ratio(CNR) were calculated. The image quality was subjectively evaluated by two radiologists using a five-point scoring system. The effective radiation dose(ED) and contrast dose were calculated and compared.

RESULTS: Group B significantly reduced radiation dose by 75.6% and contrast dose by 32.9% compared to group A. Group B exhibited higher CT values of coronary arteries than group A, and DLIR-L, DLIR-M and DLIR-H in group B provided higher SNR values and CNR values and subjective scores, among which DLIR-H had the lowest noise and highest subjective scores.

CONCLUSION: Using 70 kV combined with DLIR significantly reduces radiation and contrast dose while improving image quality in CCTA for slender patients with DLIR-H having the best effect on improving image quality.

ADVANCES IN KNOWLEDGE: The 70 kV and DLIR-H may be used in CCTA for slender patients to significantly reduce radiation dose and contrast dose while improving image quality.

PMID:40205479 | DOI:10.1093/bjr/tqaf077

J Neuroeng Rehabil. 2025 Apr 9;22(1):79. doi: 10.1186/s12984-025-01605-z.

ABSTRACT

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients' clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide an overview of the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The 201 selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

PMID:40205472 | DOI:10.1186/s12984-025-01605-z

World J Surg Oncol. 2025 Apr 9;23(1):135. doi: 10.1186/s12957-025-03774-6.

ABSTRACT

BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.

PURPOSE: To develop and test a transformer model utilizing dual-energy computed tomography (DECT) for predicting LNM in patients with PDAC.

MATERIALS AND METHODS: This retrospective study examined patients who had undergone surgical resection and had pathologically confirmed PDAC, with DECT performed between August 2016 and October 2022. Six predictive models were constructed: a DECT report model, a clinical model, 100 keV DL model, 150 keV DL model, a combined 100 + 150 keV DL model, and a model that integrated clinical information with DL-derived signatures. Multivariable logistic regression analysis was employed to develop the integrated model. The efficacy of these models was assessed by comparing their areas under the receiver operating characteristic curve (AUC) using the Delong test. Survival analysis was conducted using Kaplan-Meier curves.

RESULTS: In brief, 223 patients (mean age, 57 years ± 11 standard deviation; 93 men) were evaluated. All patients were divided into training (n = 160) and test (n = 63) sets. Patients with LNM accounted for 96 of the 223 patients (43%). In the test set, the integrated model, which integrated DECT parameters such as IC and Z, CA- 199 levels, DECT reports, and DL signatures, demonstrated the highest performance in predicting LNM, with an AUC of 0.93. In contrast, the radiologists'assessment and the clinical model yielded AUCs of 0.60 and 0.62, respectively. The integrated model-predicted positive LNM was associated with worse overall survival (hazard ratio, 1.75; 95% confidence interval: 1.22 - 2.83; P =.023).

CONCLUSION: A transformer-based model outperformed radiologists and clinical model for prediction of LNM at DECT in patients with PDAC.

PMID:40205450 | DOI:10.1186/s12957-025-03774-6

BMC Cancer. 2025 Apr 9;25(1):648. doi: 10.1186/s12885-025-13611-4.

ABSTRACT

OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies.

METHODS: GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. Utilizing the genes from these key pathways, multi-omics bioinformatics methods were employed to refine the construction of a gene signature, termed responseScore, aimed at enhancing the precision of immunotherapy response predictions. Subsequently, responseScore was explored from the perspectives of prognosis, genetic variation, pathway enrichment, and the tumor microenvironment. Concurrently, the association among 13 genes contributing to responseScore and factors such as immunotherapy response, prognosis, and the tumor microenvironment was investigated. Among these genes, PSMB6 was subjected to an in-depth analysis of its biological effect through experimental approaches like transfection and co-culture.

RESULTS: In the finalized model utilizing GNNs, it has revealed an AUC of 0.854 within the training dataset and 0.824 within the testing set, pinpointing key pathways such as R-HSA-70,268. The indicator named as responseScore excelled in its predictive accuracy regarding immunotherapy response and patient prognosis. Investigations into genetic variation, pathway enrichment, tumor microenvironment disclosed a profound association between responseScore and the enhancement of immune cell infiltration and anti-tumor immunity. A negative correlation was observed between the expression of PSMB6 and immune genes, with elevated PSMB6 expression correlating with poor prognosis. ELISA detection after co-cultivation experiments revealed significant reductions in the levels of cytokines IL-6 and IL-1β in specimens from the PCDH-PSMB6 group.

CONCLUSION: The GNNs prediction model and the responseScore developed in this research effectively indicate the immunotherapy response and prognosis for patients with skin melanoma. Additionally, responseScore provides insights into the tumor microenvironment and the characteristics of tumor immunity of melanoma. Thirteen genes identified in this study show promise as potential tumor markers or therapeutic targets. Notably, PSMB6 emerges as a potential therapeutic target for skin melanoma, where its elevated expression exhibits an inhibitory effect on the tumor immunity.

PMID:40205338 | DOI:10.1186/s12885-025-13611-4

J Imaging Inform Med. 2025 Apr 9. doi: 10.1007/s10278-025-01491-w. Online ahead of print.

ABSTRACT

This study aimed to develop and validate a predictive model for early recurrence of high-grade glioma (HGG) within 180 days, assess the prognostic value of preoperative and postoperative temporalis muscle metrics (area and thickness), and explore their significance in postoperative follow-up. Seventy-one molecularly confirmed HGG patients were included, with data sourced from local data and TCIA (The Cancer Imaging Archive) RHUH-GBM (Río Hortega University Hospital Glioblastoma) dataset. Tumor segmentation was performed using deep learning, and radiomic features were extracted following comparison with manual segmentation. Feature selection was conducted using mutual information and recursive feature elimination. A comprehensive model integrating 3D tumor radiomics and temporalis muscle metrics was developed and compared with a tumor-only model to identify the optimal predictive framework. SHAP analysis was used to evaluate model interpretability and feature importance. The TM_Tumor_HistGradientBoosting model, incorporating 16 features including temporalis muscle metrics, outperformed the tumor-only model in accuracy (0.89), recall (0.87), and F1 score (0.88). SHAP analysis highlighted that preoperative temporalis muscle cross-sectional area was strongly associated with early recurrence risk, while postoperative temporalis muscle thickness significantly contributed to recurrence prediction. Combining temporalis muscle metrics with preoperative tumor MRI substantially improved the accuracy of early recurrence prediction in HGG. Temporalis muscle metrics serve as objective and sustainable prognostic indicators with significant clinical value in postoperative follow-up.

PMID:40205255 | DOI:10.1007/s10278-025-01491-w

Nature. 2025 Apr;640(8058):470-477. doi: 10.1038/s41586-025-08829-y. Epub 2025 Apr 9.

ABSTRACT

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset2. Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.

PMID:40205215 | DOI:10.1038/s41586-025-08829-y

Nature. 2025 Apr;640(8058):368-374. doi: 10.1038/s41586-025-08854-x. Epub 2025 Apr 9.

ABSTRACT

Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning1-4, as a path towards enhanced energy efficiency and performance5-14. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore's law and Dennard scaling era15-19. Despite these advances, no photonic chip has achieved the precision necessary for practical AI applications, and demonstrations have been limited to simplified benchmark tasks. Here we introduce a photonic AI processor that executes advanced AI models, including ResNet3 and BERT20,21, along with the Atari deep reinforcement learning algorithm originally demonstrated by DeepMind22. This processor achieves near-electronic precision for many workloads, marking a notable entry for photonic computing into competition with established electronic AI accelerators23 and an essential step towards developing post-transistor computing technologies.

PMID:40205212 | DOI:10.1038/s41586-025-08854-x

J Med Ultrason (2001). 2025 Apr 9. doi: 10.1007/s10396-025-01528-1. Online ahead of print.

ABSTRACT

PURPOSE: In recent years, computer-aided diagnosis (CAD) using deep learning methods for medical images has been studied. Although studies have been conducted to classify ultrasound images of tumors of the liver into four categories (liver cysts (Cyst), liver hemangiomas (Hemangioma), hepatocellular carcinoma (HCC), and metastatic liver cancer (Meta)), no studies with additional information for deep learning have been reported. Therefore, we attempted to improve the classification accuracy of ultrasound images of hepatic tumors by adding hepatitis virus infection information to deep learning.

METHODS: Four combinations of hepatitis virus infection information were assigned to each image, plus or minus HBs antigen and plus or minus HCV antibody, and the classification accuracy was compared before and after the information was input and weighted to fully connected layers.

RESULTS: With the addition of hepatitis virus infection information, accuracy changed from 0.574 to 0.643. The F1-Score for Cyst, Hemangioma, HCC, and Meta changed from 0.87 to 0.88, 0.55 to 0.57, 0.46 to 0.59, and 0.54 to 0.62, respectively, remaining the same for Hemangioma but increasing for the rest.

CONCLUSION: Learning hepatitis virus infection information showed the highest increase in the F1-Score for HCC, resulting in improved classification accuracy of ultrasound images of hepatic tumors.

PMID:40205118 | DOI:10.1007/s10396-025-01528-1

NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3.

ABSTRACT

Transthoracic echocardiography (TTE), commonly used for initial screening of pulmonary hypertension (PH), often lacks sufficient accuracy. To address this gap, we developed and validated a multimodal fusion model for improved PH screening (MMF-PH). The study was registered in the ClinicalTrials.gov (NCT05566002, 09/30/2022). The MMF-PH underwent extensive training, validation, and testing, including comparisons with TTE and evaluations across various patient subgroups to assess robustness and reliability. We analyzed 2451 patients who underwent right heart catheterization, supplemented by a prospective dataset of 477 patients and an external dataset. The MMF-PH demonstrated robust performance across different datasets. The model outperformed TTE in terms of specificity and negative predictive value across all test datasets. An ablation study using the external test dataset confirmed the essential role of each module in the MMF-PH. The MMF-PH significantly advances PH detection, offering robust and reliable diagnostic accuracy across diverse patient populations and clinical settings.

PMID:40205021 | DOI:10.1038/s41746-025-01593-3

Respir Res. 2025 Apr 9;26(1):128. doi: 10.1186/s12931-025-03201-0.

ABSTRACT

BACKGROUND: Cellular senescence is one of the key steps in the progression of pulmonary fibrosis, and the senescence of type II alveolar epithelial cells (AEC IIs) may potentially accelerate the progression of pulmonary fibrosis. However, the molecular mechanisms underlying cellular senescence in pulmonary fibrosis remain unclear.

METHODS: The researchers first conducted in vitro experiments to investigate whether AEC IIs cultured on high matrix stiffness would lead to cellular senescence. Next, samples from mouse pulmonary fibrosis models and clinical idiopathic pulmonary fibrosis (IPF) patients were tested to observe extracellular matrix deposition, lamin A/C levels, and cellular senescence status in lung tissue. Construct lamin A/C knockdown and overexpression systems separately in AEC IIs, and observe whether changes in lamin A/C levels lead to cellular senescence. Further explore the degradation mechanism of lamin A/C using protein degradation inhibitors.

RESULTS: In vitro experiments have found that high matrix stiffness promotes senescence of AEC IIs. In a mouse model of pulmonary fibrosis, AEC IIs were found to exhibit significant cellular senescence on day 21. In clinical IPF samples, it was found that senescent cells expressed low levels of lamin A/C. In the lamin A/C SiRNA knockdown system, it was further confirmed that AEC IIs with low levels of lamin A/C are more prone to cellular senescence. Under high matrix stiffness, lamin A/C in AEC IIs is degraded through the autophagy lysosome pathway. The use of chloroquine can effectively alleviate cellular senescence.

CONCLUSIONS: High matrix stiffness degrades lamin A/C in pulmonary fibrosis through lysosomal degradation pathways, promoting AEC II senescence. Inhibition the degradation of lamin A/C could alleviate AEC II senescence.

PMID:40205454 | DOI:10.1186/s12931-025-03201-0

Arerugi. 2025;74(2):83-88. doi: 10.15036/arerugi.74.83.

ABSTRACT

A 74-year-old man was treated with nintedanib for idiopathic pulmonary fibrosis (IPF). Thirty-six days after starting to take nintedanib, he admitted to our hospital due to respiratory failure with ground-glass opacities and mediastinal emphysema on chest CT. Acute exacerbation of IPF was suspected. Because lung injury due to nintedanib was suspected, nintedanib was discontinued. Although he was treated with pulse corticosteroid therapy, interstitial pneumonia and mediastinal emphysema deteriorated. Despite continuing corticosteroid therapy, he died 21 days after admission due to respiratory failure. Subsequently drug lymphocyte stimulation test for nintedanib was found to be positive. We should pay attention to possibility of drug-induced lung injury caused by nintedanib.

PMID:40204485 | DOI:10.15036/arerugi.74.83

Theor Appl Genet. 2025 Apr 9;138(5):95. doi: 10.1007/s00122-025-04877-0.

ABSTRACT

Understanding the genetic basis of nutrient accumulation in wheat is crucial for improving its nutritional content and addressing global food security challenges. Here, we identified stable pleiotropic loci controlling the accumulation of 13 nutritional elements in wheat across diverse environments using a large wheat population of 1470 individuals. Our analysis revealed significant variability in SNP-based heritability values across 13 essential elements. Genetic correlations among elements uncovered complex relations, with positive correlations observed within two distinct groups, where calcium (Ca), cobalt (Co), potassium (K), and sodium (Na) formed one group, and copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), phosphorus (P), and zinc (Zn) formed the other. Negative correlations were observed among elements across both groups. Through MetaGWAS analysis, we identified stable QTL associated with individual elements and elements with high positive correlations. We identified 67 stable QTL across environments that are independent from grain yield, of which 56 were detected using the MetaGWAS analysis indicating their pleiotropic effect on multiple elements. A major QTL on chromosome 7D that can shift the phenotype up to one standard deviation compared to the mean phenotype in the population exhibited differential effects on multiple elements belonging to both groups. Our findings offer novel insights into the genetic architecture of nutrient accumulation in wheat and have practical implications for breeding programmes aimed at enhancing multiple nutrients simultaneously. By targeting stable QTL, breeders can develop wheat varieties with improved nutritional profiles, contributing to global food security and human health.

PMID:40205176 | DOI:10.1007/s00122-025-04877-0

Nature. 2025 Apr 9. doi: 10.1038/s41586-025-08878-3. Online ahead of print.

ABSTRACT

Human cells consist of a complex hierarchy of components, many of which remain unexplored1,2. Here we construct a global map of human subcellular architecture through joint measurement of biophysical interactions and immunofluorescence images for over 5,100 proteins in U2OS osteosarcoma cells. Self-supervised multimodal data integration resolves 275 molecular assemblies spanning the range of 10-8 to 10-5 m, which we validate systematically using whole-cell size-exclusion chromatography and annotate using large language models3. We explore key applications in structural biology, yielding structures for 111 heterodimeric complexes and an expanded Rag-Ragulator assembly. The map assigns unexpected functions to 975 proteins, including roles for C18orf21 in RNA processing and DPP9 in interferon signalling, and identifies assemblies with multiple localizations or cell type specificity. It decodes paediatric cancer genomes4, identifying 21 recurrently mutated assemblies and implicating 102 validated new cancer proteins. The associated Cell Visualization Portal and Mapping Toolkit provide a reference platform for structural and functional cell biology.

PMID:40205054 | DOI:10.1038/s41586-025-08878-3

Nature. 2025 Apr 9. doi: 10.1038/s41586-025-08864-9. Online ahead of print.

ABSTRACT

Given the large number of genes significantly associated with risk for neuropsychiatric disorders, a critical unanswered question is the extent to which diverse mutations-sometimes affecting the same gene-will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, which encodes a presynaptic cell-adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain and are differentially affected by unique (non-recurrent) deletions1. We contrast the cell-type-specific effect of patient-specific mutations in NRXN1 using human-induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Through distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/- deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Reciprocal isogenic manipulations causally demonstrate that aberrant splicing drives these changes in synaptic activity. For NRXN1 deletions, and perhaps more broadly, precision medicine will require stratifying patients based on whether their gene mutations act through LOF or GOF mechanisms, to achieve individualized restoration of NRXN1 isoform repertoires by increasing wild-type and/or ablating mutant isoforms. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disorders, our findings add nuance to future considerations of precision medicine.

PMID:40205044 | DOI:10.1038/s41586-025-08864-9

Cell Death Differ. 2025 Apr 9. doi: 10.1038/s41418-025-01502-x. Online ahead of print.

ABSTRACT

The stress-associated chaperone system is an actionable target in cancer therapies. It is ubiquitously upregulated in cancer tissues and enables tumorigenicity by stabilizing oncoproteins. Most inhibitors target the key component, heat-shock protein 90 (HSP90). Although HSP90 inhibitors are highly tumor-selective, they fail in clinical trials. These failures are partly due to interference with a negative regulatory feedback loop in the heat-shock response (HSR): in response to HSP90 inhibition, there is compensatory synthesis of stress-inducible chaperones, mediated by the transcription factor heat-shock-factor 1 (HSF1). We recently identified that wild-type p53 reduces the HSR by repressing HSF1 via a p21-CDK4/6-MAPK-HSF1 axis. Here, we test whether in HSP90-based therapies, simultaneous p53 activation or direct cell cycle inhibition interrupts the deleterious HSF1-HSR axis and improves the efficiency of HSP90 inhibitors. We found that the clinically relevant p53 activator Idasanutlin suppresses the HSF1-HSR activity in HSP90 inhibitor-based therapies. This combination synergistically reduces cell viability and accelerates cell death in p53-proficient colorectal cancer (CRC) cells, murine tumor-derived organoids, and patient-derived organoids (PDOs). Mechanistically, upon combination therapy, CRC cells upregulate p53-associated pathways, apoptosis, and inflammatory pathways. Likewise, in a CRC mouse model, dual HSF1-HSP90 inhibition represses tumor growth and remodels immune cell composition. Importantly, inhibition of the cyclin-dependent kinases 4/6 (CDK4/6) under HSP90 inhibition phenocopies synergistic repression of the HSR in p53-proficient CRC cells. Moreover, in p53-deficient CRC cells, HSP90 inhibition in combination with CDK4/6 inhibitors similarly suppresses the HSF1-HSR and reduces cancer growth. Likewise, p53-mutated PDOs respond to dual HSF1-HSP90 inhibition, providing a strategy to target CRC independent of the p53 status. In sum, we provide new options to improve HSP90-based therapies to enhance CRC therapies.

PMID:40204953 | DOI:10.1038/s41418-025-01502-x

Nat Commun. 2025 Apr 9;16(1):3351. doi: 10.1038/s41467-025-57894-4.

ABSTRACT

Despite extensive research, the gene regulatory architecture governing mammalian cell states remains poorly understood. Here we present an integrative systems biology approach to elucidate the network architecture of primed state pluripotency. Using an unbiased methodology, we identified and experimentally confirmed 132 transcription factors as master regulators (MRs) of mouse epiblast stem cell (EpiSC) pluripotency, many of which were further validated by CRISPR-mediated functional assays. To assemble a comprehensive regulatory network, we silenced each of the 132 MRs to assess their effects on the other MRs and their transcriptional targets, yielding a network of 1273 MR → MR interactions. Network architecture analyses revealed four functionally distinct MR modules (communities), and identified key Speaker and Mediator MRs based on their hierarchical rank and centrality. Our findings elucidate the de-centralized logic of a "communal interaction" model in which the balanced activities of four MR communities maintain primed state pluripotency.

PMID:40204698 | DOI:10.1038/s41467-025-57894-4

J Biotechnol. 2025 Apr 7:S0168-1656(25)00088-4. doi: 10.1016/j.jbiotec.2025.04.002. Online ahead of print.

ABSTRACT

Xylose isomerases (XI, EC 5.3.1.5) are key enzymes for the metabolism of pentoses by microorganisms. The importance of XIs goes beyond academic biochemical research and the catalysis of aldo-ketose conversion by XIs is among the most successful examples of industrial enzyme technology in a market that generates multibillion dollar annual revenues. Here we present an in-depth review of how structural information has contributed to the current understanding of XI catalysis, and discuss topics related to the ongoing efforts to elucidate key aspects of the catalytic mechanism. An overview of XI immobilization is also provided that illustrates how the discoveries in basic enzyme technology research can generate opportunities for novel uses of XI, and we review not only historical aspects but also more recent applications in HFCS, biofuels and other applications. The systems biology revolution will impact all aspects of XI research and application, and we finalize by reviewing the contemporary efforts of metabolic and protein engineering using XI and the future roles of the enzyme in the expanding bioeconomy.

PMID:40204218 | DOI:10.1016/j.jbiotec.2025.04.002

Dev Cell. 2025 Apr 4:S1534-5807(25)00157-1. doi: 10.1016/j.devcel.2025.03.010. Online ahead of print.

ABSTRACT

Cytokinesis challenges epithelial tissue homeostasis by generating forces that pull on neighboring cells. Junction reinforcement at the furrow in Xenopus epithelia regulates the speed of furrowing, suggesting that cytokinesis is subject to resistive forces from epithelial neighbors. We show that contractility factors accumulate near the furrow in neighboring cells, and increasing neighbor cell stiffness slows furrowing. Optogenetically increasing contractility in one or both neighbor cells slows furrowing or induces cytokinetic failure. Uncoupling mechanotransduction between dividing cells and their neighbors increases the furrow ingression rate, alters topological cell packing following cytokinesis, and impairs barrier function at the furrow. Computational modeling validates our findings and provides additional insights about epithelial mechanics during cytokinesis. We conclude that forces from the cytokinetic array must be carefully balanced with restraining forces generated by neighbor cells to regulate the speed and success of cytokinesis and maintain epithelial homeostasis.

PMID:40203834 | DOI:10.1016/j.devcel.2025.03.010

Microbiology (Reading). 2025 Apr;171(4). doi: 10.1099/mic.0.001553.

ABSTRACT

Cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy has significantly changed the course of the disease in people with cystic fibrosis (CF) (pwCF). The approved triple therapy of elexacaftor, tezacaftor and ivacaftor (ETI), commercially known as Trikafta, increases CFTR channel function, leading to improvements in sweat chloride concentration, exercise capacity, body mass index, lung function and chronic respiratory symptoms. Because of this, the majority of pwCF are living longer and having fewer CF exacerbations. However, colonization with the common CF respiratory pathogens persists and remains a major cause of morbidity and mortality. Here, we review the current literature on the effect of ETI on the respiratory microbiota and discuss the challenges in addressing CF lung infections in the era of these new life-extending therapies.

PMID:40202901 | DOI:10.1099/mic.0.001553

PLoS One. 2025 Apr 9;20(4):e0319299. doi: 10.1371/journal.pone.0319299. eCollection 2025.

ABSTRACT

Traditional crack detection methods initially relied on manual observation, followed by instrument-assisted techniques. Today, road surface inspection leverages deep learning to achieve automated crack detection. However, in the domain of deep learning-based road surface damage classification, the heterogeneous and complex nature of road environments introduces significant background noise and unstructured features. These factors often undermine the robustness and generalization capability of models, thereby adversely affecting classification accuracy. To address this challenge, this research incorporates edge priors by integrating traditional edge detection techniques with deep convolutional neural networks (DCNNs). This paper proposes an innovative mechanism called Edge-Enhanced Multi-Scale Feature Fusion (EE-MSFF), which enhances edge information through multi-scale feature extraction, thereby mitigating the impact of complex backgrounds and improving the model's focus on crack regions. Specifically, the proposed mechanism leverages classical edge detection operators such as Sobel, Prewitt, and Laplacian to perform multi-scale edge information extraction during the feature extraction phase of the model. This process captures both local edge features and global structural information in crack regions, thereby enhancing the model's resistance to interference from complex backgrounds. By employing multi-scale receptive fields, the EE-MSFF mechanism facilitates hierarchical fusion of feature maps, guiding the model to learn edge information that is correlated with crack regions. This effectively strengthens the model's ability to perceive damaged pavement features in complex environments, improving classification accuracy and stability. In this study, the model underwent systematic training and validation on both the complex-background dataset RDD2020 and the simple-background dataset Concrete_Data_Week3. Experimental results demonstrate that the proposed model achieved a classification accuracy of 88.68% on the RDD2020 dataset and 99.5% on the Concrete_Data_Week3 dataset, where background interference is minimal. Furthermore, ablation studies were conducted to analyze the independent contributions of each module, highlighting the performance improvements associated with the integration of multi-scale edge features.

PMID:40203245 | DOI:10.1371/journal.pone.0319299