J Gastrointest Cancer. 2025 Apr 16;56(1):99. doi: 10.1007/s12029-025-01227-7.

ABSTRACT

BACKGROUND: The pathological response in rectal cancer treatment provides insight into the molecular mechanisms, including genetic alterations and signaling pathways that influence tumor behavior and resistance to treatment.

CASE PRESENTATION: This report describes a 34-year-old Iraqi male diagnosed with stage III rectal cancer who achieved a complete pathological response following treatment with oxaliplatin-based chemotherapy. Notably, this outcome was achieved without the administration of chemoradiotherapy or the occurrence of neurotoxicity despite the efficacious cumulative‑dose administration (1700 mg/m2) of oxaliplatin. Genomic analysis revealed the presence of a heterozygous (Ile/Val) genotype in the GSTP1 gene, which may have contributed to the observed treatment response.

CONCLUSIONS: Genetic biomarkers play a crucial role in refining treatment strategies by enabling a more precise selection of patients who may safely forgo radiotherapy, thereby minimizing its associated toxicities. Additionally, molecular profiling can help predict susceptibility to oxaliplatin-induced neurotoxicity, facilitating dose adjustments or alternative therapeutic approaches to enhance treatment tolerance and long-term quality of life. Our findings highlight the importance of molecular profiling in optimizing treatment strategies while minimizing toxicity, especially in situations where radiological assessments suggest residual disease or produce unclear results.

PMID:40240738 | DOI:10.1007/s12029-025-01227-7

EBioMedicine. 2025 Apr 11:105675. doi: 10.1016/j.ebiom.2025.105675. Online ahead of print.

ABSTRACT

BACKGROUND: Epilepsy is one of the most common neurological disorders, affecting over 50 million people worldwide. One-third of people with epilepsy do not respond to currently available anti-seizure medications, constituting one of the most important problems in epilepsy. Little is known about the molecular pathology of drug resistance in epilepsy, in particular, possible underlying genetic factors are largely unknown.

METHODS: We performed a genome-wide association study (GWAS) in two epilepsy cohorts of European ancestry, comparing drug-resistant (N = 4208) to drug-responsive individuals (N = 2618) followed by meta-analyses across the studies. Next, we performed subanalyses split into two broad subtypes: acquired or non-acquired focal and genetic generalized epilepsy.

FINDINGS: Our drug-resistant versus drug-responsive epilepsy GWAS meta-analysis showed no significant loci when combining all epilepsy types. Sub-analyses on individuals with focal epilepsy (FE) identified a significant locus on chromosome 1q42.11-q42.12 (lead SNP: rs35915186, P = 1·51 × 10-8, OR[C] = 0·74). This locus was not associated with any epilepsy subtype in the latest epilepsy GWAS (lowest uncorrected P = 0·009 for FE vs. healthy controls), and drug resistance in FE was not genetically correlated with susceptibility to FE itself. Seven genome-wide significant SNPs within this locus, encompassing the genes CNIH4, WDR26, and CNIH3, were identified to protect against drug-resistant FE. Further transcriptome-wide association studies (TWAS) imply significantly higher expression levels of CNIH3 and WDR26 in drug-resistant FE than in drug-responsive FE. CNIH3 is implicated in AMPA receptor assembly and function, while WDR26 haploinsufficiency is linked to intellectual disability and seizures. These findings suggest that CNIH3 and WDR26 may play a role in mediating drug response in focal epilepsy.

INTERPRETATION: We identified a contribution of common genetic variation to drug-resistant focal epilepsy. These findings provide insights into possible mechanisms underlying drug response variability in epilepsy, offering potential targets for personalised treatment approaches.

FUNDING: This work is part of the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 279062 (EpiPGX) and the Centers for Common Disease Genomics (CCDG) program, funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI).

PMID:40240269 | DOI:10.1016/j.ebiom.2025.105675

J Infect Dis. 2025 Apr 17:jiaf195. doi: 10.1093/infdis/jiaf195. Online ahead of print.

ABSTRACT

BACKGROUND: Variability in the pharmacokinetics (PK) of first-line anti-tuberculosis drugs (rifampicin -RIF, isoniazid -INH and pyrazinamide (PZA)) is high and may be influenced by pharmacogenetic polymorphism. We performed a pharmacogenetic substudy in 90 participants with PK data from the HIRIF trial in Peru.

METHODS: Relevant single nucleotide polymorphisms (SNPs) in the NAT2, SLCO1B1, AADAC and AOX1 locii were genotyped using real time PCR.

RESULTS: The proportions of slow, intermediate and fast acetylators predicted by a conventional six-SNP NAT2 panel were 32.5%, 48.2% and 19.2% respectively. A single NAT2 tag SNP (rs1495741) agreed with the panel-predicted phenotype in 91% and was a better predictor of INH AUC. Accounting for discrepancies possibly caused by rare alleles not represented in the panel or that could be unequivocally resolved using observed AUC, sensitivity of the tag SNP was 97.7%. A previously described SNP in SLCO1B1 (rs4149032) was present at an allele frequency of 0.31 and appeared to influence RIF AUC and Cmax at a dose of 20 mg/kg, despite an extreme distribution of alleles across the randomised arms The AADAC SNP (rs1803155) predominated in the study population and was not linked to RIF PK, though an effect could have been missed due to sample size and allele frequency .There was no association between PZA PK and a common SNP in AOX1 (rs55754655).

CONCLUSIONS: A tag SNP approach may offer simpler and cheaper prediction of INH PK. Further exploration of the impact of SLCO1B1 SNPs on RIF PK is required in this and other populations.

PMID:40239986 | DOI:10.1093/infdis/jiaf195

Arch Virol. 2025 Apr 16;170(5):106. doi: 10.1007/s00705-025-06282-w.

ABSTRACT

The escalating challenges of antibiotic resistance in bacterial pathogens have necessitated the exploration of alternative therapeutic strategies. Among these, bacteriophage therapy has regained attention as a promising approach to combat multidrug-resistant bacteria. Bacteriophages are viruses that infect and lyse specific bacterial strains, making them attractive candidates for targeted antimicrobial treatment. Burkholderia multivorans, a Gram-negative bacterium, is known to cause opportunistic infections, particularly in individuals with a compromised immune system or with cystic fibrosis. The prevalence of antibiotic-resistant Burkholderia strains has raised concerns about treatment options. In this study, we characterized the Burkholderia phage Bm1, a virulent bacteriophage isolated from an environmental source. Electron microscopy revealed that Bm1 phage particles have myovirus morphology, with an icosahedral head of 72 nm in diameter and a contractile tail of 100 nm in length and 18 nm in width. The genome of phage Bm1 consists of a double-stranded DNA of 67,539 bp with a terminal repeat region at each end. Comparative analysis indicated that the closest relative of phage Bm1 is Burkholderia phage BCSR129, with a calculated VIRIDIC identity of 57.7%. The apparent absence of an integrase gene suggests that the Burkholderia phage Bm1 has a strictly lytic life cycle.

PMID:40240564 | DOI:10.1007/s00705-025-06282-w

Eur Radiol. 2025 Apr 16. doi: 10.1007/s00330-025-11589-y. Online ahead of print.

ABSTRACT

OBJECTIVES: MRI detects abnormal lung perfusion in patients with cystic fibrosis (CF). However, little is known about the contribution of bronchial arteries to lung perfusion in CF. We hypothesized that delayed perfusion can be detected by dynamic contrast-enhanced (DCE-)MRI and that bronchial artery dilatation (BAD) is associated with changes in lung perfusion.

MATERIALS AND METHODS: Morpho-functional MRI was prospectively acquired in 75 patients with CF (18.7 ± 7.6 years, range 6-39 years). Lungs and perfusion defects were segmented automatically to quantify perfusion defects in percent (QDP). Pulmonary blood flow (PBF), mean transit time (MTT), and perfusion delay were calculated for the whole lung, inside normally perfused and perfusion defect areas. Chest MRI score and BAD were assessed visually.

RESULTS: QDP and PBF correlated with MRI global score (r = 0.58 and -0.53, p < 0.001). In normally perfused lung, PBF was higher (161.2 ± 77.9 mL/100 mL/min vs. 57.5 ± 26.4 mL/100 mL/min, p < 0.001), and MTT (5.4 ± 1.7 s vs. 6.9 ± 2.3 s, p < 0.001) and perfusion delay were shorter than in perfusion defect areas (4.6 ± 5.3 s vs. 13.4 ± 16.2 s, p < 0.001). 48 (64.0%) patients showed BAD, had higher QDP (44.6 ± 20.8% vs. 17.3 ± 11.0%, p < 0.001) and lower PBF (91.9 ± 54.8 mL/100 mL/min vs. 178.3 ± 77.4 mL/100 mL/min, p < 0.001) than patients without BAD. MTT was shorter (6.3 ± 1.9 s vs. 8.0 ± 2.6 s, p < 0.001), and perfusion delay was longer (13.8 ± 10.1 s vs. 12.8 ± 23.7 s, p < 0.02) inside perfusion defects of patients with BAD compared to without BAD.

CONCLUSION: Perfusion parameters correlate with lung disease severity, and perfusion defects showed delayed perfusion in patients with CF. BAD was associated with more extensive perfusion defects and reduced PBF.

KEY POINTS: Question Dilated bronchial arteries are a common comorbidity in cystic fibrosis (CF), which can cause hemoptysis, but their quantitative contribution to lung perfusion is little researched. Findings Perfusion defects in percent (QDP) enabled objective assessment of perfusion abnormalities in CF patients, while perfusion delay and arterial correlation showed bronchial artery perfusion contribution. Clinical relevance The usage of quantitative perfusion metrics in CF may help tracking disease progression. By also including the proposed metrics perfusion delay and arterial correlation, bronchial artery inflow could be assessed and used to detect early onset of bronchial artery dilation.

PMID:40240556 | DOI:10.1007/s00330-025-11589-y

Eur J Hum Genet. 2025 Apr 16. doi: 10.1038/s41431-025-01835-8. Online ahead of print.

ABSTRACT

Using the Australian Census survey 2021 as base population, a microsimulation model, PreconMOD was developed to evaluate the cost-effectiveness of population-based expanded reproductive carrier screening (RCS) for 569 recessive conditions from the health service and societal perspectives. The model simulated the effect of expanded RCS including the downstream interventions for at-risk couples on cost and outcomes. The comparators were (i) no population screening (ii) limited screening for cystic fibrosis, spinal muscular atrophy, and fragile X syndrome and (iii) a 300 conditions screening panel. Averted affected births and health service cost with expanded RCS were projected to year 2061. At a 50% uptake, our model predicts that expanded RCS is cost saving (i.e., higher quality-adjusted life-years and lower costs) compared with other screening strategies in the model from the health service and societal perspectives. The number of affected births averted in a single cohort would increase from 84 [95% confidence interval (CI) 60-116] with limited screening to 2067 (95%CI 1808-2376) with expanded RCS. Expanded RCS was cost-saving compared to the 300-conditions screening panel. Indirect cost accounted for about one-third of the total costs associated with recessive disorders. Our model predicts that the direct treatment cost associated with current limited 3 genes screening would increase by 20% each year to A$73.4 billion to the health system by 2061. Our findings contribute insights on the cost burden of genetic diseases and the economic benefits of expanded RCS to better informed resource allocation decisions.

PMID:40240435 | DOI:10.1038/s41431-025-01835-8

J Cyst Fibros. 2025 Apr 16:S1569-1993(25)00769-6. doi: 10.1016/j.jcf.2025.04.003. Online ahead of print.

ABSTRACT

BACKGROUND: Highly effective modulator therapies (HEMT) are increasing the lifespan for many people with cystic fibrosis (pwCF), making it necessary to identify and understand CF specific age-related consequences. In this study, we examine the impact of aging on cognitive function and age-related brain pathology in a CF mouse model focusing on phospho-Tau (pTau) pathology.

METHODS: Cognitive function was measured by novel object recognition and spontaneous alternation behavior tests. Hippocampal neuronal function was assessed by measuring long-term potentiation (LTP) electrophysiology, the synaptic correlate of learning and memory. Tau pathology was assessed by immunohistochemical analyses and western blot assessment of pTau levels in CF mouse brain, as well as human nasal epithelial cells isolated from pwCF.

RESULTS: Cognitive function declined progressively with age in Cftr (G542X/G542X) (G542X) mice, a model of CF, compared to wild-type (WT) littermate controls. LTP was also deficient in older G542X mice. Increased pTau was observed by staining and western blot analysis in the hippocampus of aged CF mice. Secondary impacts of tauopathy, including increased microglial uptake of cholesterol and reduced neuronal density were also observed. Lastly, human nasal epithelial cells from pwCF were found to display elevated pTau levels compared to non-CF controls.

CONCLUSIONS: Aging CF mice develop tauopathy, cognitive decline, LTP impairment, microglial activation, and neurodegeneration that is not experienced by age-matched WT littermates, a condition herein termed cystic fibrosis-related neurodegeneration (CFND). These findings suggest that pwCF may be at risk for tauopathy-related neurodegeneration and cognitive impairment with aging.

PMID:40240239 | DOI:10.1016/j.jcf.2025.04.003

Eur Respir Rev. 2025 Apr 16;34(176):240227. doi: 10.1183/16000617.0227-2024. Print 2025 Apr.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) registries capture important information in high-burden health domains to support improvement in health outcomes, although a number of unanswered questions persist, as follows. 1) Do CF registries utilise implementation science strategies to improve patient outcomes? 2) Which implementation strategies have been engaged? 3) Has the engagement of these strategies been effective in improving clinical outcomes?

METHODS: We undertook a systematic review to exploring the use of implementation science strategies by CF registries for healthcare improvement. We searched MEDLINE, Embase, Scopus, Emcare and Web of Science databases for use of Expert Recommendations for Implementing Change (ERIC) implementations and use of the Knowledge to Action framework for improvement. We used the Risk of Bias in Non-randomised Studies - of Interventions tool for risk-of-bias assessment.

RESULTS: 1974 citations were identified and 12 studies included. Included studies described 45 ERIC implementation strategies from nine categories. Strategies included "use evaluative and iterative strategies" (n=9) and "develop stakeholder interrelationships" (n=10). Least-used strategies were "utilise financial strategies" (n=1), "support clinicians" category (n=3) and "provide interactive assistance" (n=2). All 12 studies utilised monitoring of knowledge use, and assessing barriers and facilitators of knowledge use. Only seven studies utilised mechanisms to sustain knowledge use.

DISCUSSION: Reported studies describe significant benefits in important CF outcomes for people with CF reported at site-specific and population levels. Studies highlighted the importance of governance, leadership, patient and family engagement, multidisciplinary engagement, quality improvement, data and analytics and research. The ready availability of clinical performance data feedback to clinicians and patients by CF registries is likely to strengthen the effectiveness of CF registries in driving healthcare improvement within a learning health system.

PMID:40240058 | DOI:10.1183/16000617.0227-2024

J Nanobiotechnology. 2025 Apr 16;23(1):295. doi: 10.1186/s12951-025-03339-5.

ABSTRACT

BACKGROUND: Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7-15 days) and large-field view (e.g., > 1000 × 1000 μm2). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion.

RESULTS: Herein, we develop a molecular imaging strategy for minimally invasive lymphoma diagnosis. By spreading lymphoma tissue sections tightly on a surface-enhanced Raman scattering (SERS) chip, label-free images of DNA double strand breaks (DSBs) in 30 × 30 μm2 tissue sections could be achieved in ~ 15 min. To establish a proof of concept, the Raman image datasets collected from clinical samples of normal lymphatic tissues and non-Hodgkin's lymphoma (NHL) tissues were well organized and trained in a deep convolutional neural network model, finally achieving a recognition rate of ~ 91.7 ± 2.1%.

CONCLUSIONS: The molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma.

PMID:40241186 | DOI:10.1186/s12951-025-03339-5

J Neuroeng Rehabil. 2025 Apr 16;22(1):84. doi: 10.1186/s12984-025-01625-9.

ABSTRACT

Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particularly crucial. Not all rehabilitation outcomes are favorable, highlighting the necessity for personalized rehabilitation. Precision assessment is essential for tailored rehabilitation interventions. Wearable inertial measurement units (IMUs) and deep learning approaches have been effectively employed for motor function prediction. This study aims to use machine learning techniques and data collected from IMUs to assess the Fugl-Meyer upper extremity subscale for post-stroke patients with motor dysfunction. IMUs signals from 120 patients were collected during a clinical trial. These signals were fed into a gated recurrent unit network to complete the scoring of individual actions, which were then aggregated to obtain the total score. Simultaneously, on the basis of the internal correlation between the Fugl-Meyer assessment and the Brunnstrom scale, Brunnstrom stage prediction models of the arm and hand were established via the random forest and extremely randomized trees algorithm. The experimental results show that the proposed models can score Fugl-Meyer items with a high accuracy of 92.66%. The R2 between the doctors' score and the model's score is 0.9838. The Brunnstrom stage prediction models can predict high-quality stages, achieving a Spearman correlation coefficient of 0.9709. The application of the proposed method enables precision assessment of patients' upper extremity motor function, thereby facilitating more personalized rehabilitation programs to achieve optimal recovery outcomes. Trial registration: Clinical trial of telerehabilitation training and intelligent evaluation system, ChiCTR2200061310, Registered 20 June 2022-Retrospective registration.

PMID:40241161 | DOI:10.1186/s12984-025-01625-9

BioData Min. 2025 Apr 16;18(1):31. doi: 10.1186/s13040-025-00445-w.

ABSTRACT

BACKGROUND: Functional deterioration (FD) of various organ systems is the major cause of death in ICU patients, but few studies propose effective multi-task (MT) model to predict FD of multiple organs simultaneously. This study propose a MT deep learning model named inter-organ correlation based multi-task model (IOC-MT), to dynamically predict FD in six organ systems.

METHODS: Three public ICU databases were used for model training and validation. The IOC-MT was designed based on the routine MT deep learning framework, but it used a Graph Attention Networks (GAT) module to capture inter-organ correlation and an adaptive adjustment mechanism (AAM) to adjust prediction. We compared the IOC-MT to five single-task (ST) baseline models, including three deep models (LSTM-ST, GRU-ST, Transformer-ST) and two machine learning models (GRU-ST, RF-ST), and performed ablation study to assess the contribution of important components in IOC-MT. Model discrimination was evaluated by AUROC and AUPRC, and model calibration was assessed by the calibration curve. The attention weight and adjustment coefficient were analyzed at both overall and individual level to show the AAM of IOC-MT.

RESULTS: The IOC-MT had comparable discrimination and calibration to LSTM-ST, GRU-ST and Transformer-ST for most organs under different gap windows in the internal and external validation, and obviously outperformed GRU-ST, RF-ST. The ablation study showed that the GAT, AAM and missing indicator could improve the overall performance of the model. Furthermore, the inter-organ correlation and prediction adjustment of IOC-MT were intuitive and comprehensible, and also had biological plausibility.

CONCLUSIONS: The IOC-MT is a promising MT model for dynamically predicting FD in six organ systems. It can capture inter-organ correlation and adjust the prediction for one organ based on aggregated information from the other organs.

PMID:40241105 | DOI:10.1186/s13040-025-00445-w

BMC Musculoskelet Disord. 2025 Apr 17;26(1):378. doi: 10.1186/s12891-025-08631-x.

ABSTRACT

RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classification.

MATERIALS AND METHODS: A total of 456 participants were retrospectively included and were divided into a development cohort comprising 355 patients, with a 7:3 ratio randomly assigned to the training and validation cohorts, and a test cohort comprising 101 patients. The automatic segmentation model for the proximal femur was trained using VB-Net. The Dice similarity coefficient (DSC) and volume difference (VD) were employed to evaluate the performance of the segmentation model. A three-classification predictive model for assessing bone mineral status was constructed utilizing radiomic analysis. The diagnostic performance of the radiomics model was assessed using the area under the curve (AUC), sensitivity, and specificity.

RESULTS: The automatic segmentation model for the proximal femur demonstrated excellent performance, achieving DSC values of 0.975 ± 0.012 and 0.955 ± 0.137 in the validation and test cohorts, respectively. In the test cohort, the radiomics model utilizing the random forest (RF) classifier achieved AUC values, sensitivity, and specificity of 0.924 (95% CI: 0.854-0.967), 0.846 (95% CI: 0.719-0.931), and 0.837 (95% CI: 0.703-0.927) for the identification of normal bone mass. For the identification of osteoporosis, the corresponding metrics were 0.960 (95% CI: 0.913-1.000), 0.947 (95% CI: 0.740-0.999), and 0.963 (95% CI: 0.897-0.992). In the case of osteopenia, the corresponding metrics were 0.828 (95% CI: 0.747-0.909), 0.767 (95% CI: 0.577-0.901), and 0.746 (95% CI: 0.629-0.842).

CONCLUSION: A three-classification predictive model combining a deep learning-based automatic segmentation of the proximal femur and a radiomics-based bone status classification on LDCT images can be used for the opportunistic detection of osteoporosis.

PMID:40241032 | DOI:10.1186/s12891-025-08631-x

Sci Rep. 2025 Apr 16;15(1):13074. doi: 10.1038/s41598-025-97842-2.

ABSTRACT

Deep learning-based image processing methods are commonly used for bridge crack detection. Aiming at the problem of missed detections and false positives caused by light, stains, and dense cracks during detection, this paper proposes a bridge crack detection algorithm based on the improved YOLOv8n model. Firstly, enhancing the model's feature extraction capabilities by incorporating the global attention mechanism into the Backbone and Neck to gather additional crack characterization information. And optimizing the original feature fusion model through Gam-Concat to enhance the feature fusion effect. Subsequently, in the FPN-PAN structure, replacing the original upsample module with DySample promotes the full fusion of high- and low-resolution feature information, enhancing the detection capability for cracks of different scales. Finally, adding MPDIoU to the Head to optimize the bounding box function loss, enhancing the model's ability to evaluate the overlap of dense cracks and better reflecting the spatial relationships between the cracks. In ablation and comparison experiments, the improved model achieved increases of 3.02%, 3.39%, 2.26%, and 0.81% in mAP@0.5, mAP@0.5:0.95, precision, and recall, respectively, compared to the original model. And the detection accuracy is significantly higher than other comparative models. It has practical application value in bridge inspection projects.

PMID:40240806 | DOI:10.1038/s41598-025-97842-2

Sci Rep. 2025 Apr 16;15(1):13075. doi: 10.1038/s41598-025-97407-3.

ABSTRACT

Multi-Criteria Recommender Systems (MCRSs) improve personalization by incorporating multiple user preferences. However, their application in Technology-Enhanced Learning (TEL) remains limited due to challenges such as data sparsity, over-specialization, and cold-start problems. Traditional techniques, such as Singular Value Decomposition (SVD) and SVD + + , struggle to effectively model the complex interactions within multi-criteria rating data, leading to suboptimal recommendations. This paper introduces a hybrid DeepFM-SVD + + model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations. The model captures both low-order feature interactions using factorization machines and high-order dependencies through deep neural networks, enabling more adaptive recommendations. To evaluate its performance, the model is tested on two multi-criteria datasets: ITM-Rec (TEL domain) and Yahoo Movies (non-TEL domain). The experimental results show that DeepFM-SVD + + consistently outperforms the traditional techniques across multiple evaluation metrics. The findings highlight significant improvements in accuracy, demonstrating the model's effectiveness in sparse datasets and its generalization across domains. By addressing the limitations of existing MCRS techniques, this study contributes to advancing personalized learning recommendations in TEL and expands the applicability of deep learning-based MCRS beyond educational contexts.

PMID:40240805 | DOI:10.1038/s41598-025-97407-3

Sci Rep. 2025 Apr 16;15(1):13171. doi: 10.1038/s41598-025-96035-1.

ABSTRACT

X-ray image-based prohibited item detection plays a crucial role in modern public security systems. Despite significant advancements in deep learning, challenges such as feature extraction, object occlusion, and model complexity remain. Although recent efforts have utilized larger-scale CNNs or ViT-based architectures to enhance accuracy, these approaches incur substantial trade-offs, including prohibitive computational overhead and practical deployment limitations. To address these issues, we propose Xray-YOLO-Mamba, a lightweight model that integrates the YOLO and Mamba architectures. Key innovations include the CResVSS block, which enhances receptive fields and feature representation; the SDConv downsampling block, which minimizes information loss during feature transformation; and the Dysample upsampling block, which improves resolution recovery during reconstruction. Experimental results demonstrate that the proposed model achieves superior performance across three datasets, exhibiting robust performance and excellent generalization ability. Specifically, our model attains mAP50-95 of 74.6% (CLCXray), 43.9% (OPIXray), and 73.9% (SIXray), while demonstrating lightweight efficiency with 4.3 M parameters and 10.3 GFLOPs. The architecture achieves real-time performance at 95.2 FPS on the GPUs. In summary, Xray-YOLO-Mamba strikes a favorable balance between precision and computational efficiency, demonstrating significant advantages.

PMID:40240781 | DOI:10.1038/s41598-025-96035-1

Neuroinformatics. 2025 Apr 16;23(2):28. doi: 10.1007/s12021-025-09724-7.

ABSTRACT

Brain slicing is a commonly used technique in brain science research. In order to study the spatial distribution of labeled information, such as specific types of neurons and neuronal circuits, it is necessary to register the brain slice images to the 3D standard brain space defined by the reference atlas. However, the registration of 2D brain slice images to a 3D reference brain atlas still faces challenges in terms of accuracy, computational throughput, and applicability. In this paper, we propose the SlicesMapi, an interactive 3D registration method for brain slice sequence. This method corrects linear and non-linear deformations in both 3D and 2D spaces by employing dual constraints from neighboring slices and corresponding reference atlas slices and guarantees precision by registering images with full resolution, which avoids the information loss of image down-sampling implemented in the deep learning based registration methods. This method was applied to deal the challenges of unknown slice angle registration and non-linear deformations between the 3D Allen Reference Atlas and slices with cytoarchitectonic or autofluorescence channels. Experimental results demonstrate Dice scores of 0.9 in major brain regions, highlighting significant advantages over existing methods. Compared with existing methods, our proposed method is expected to provide a more accurate, robust, and efficient spatial localization scheme for brain slices. Therefore, the proposed method is capable of achieving enhanced accuracy in slice image spatial positioning.

PMID:40240690 | DOI:10.1007/s12021-025-09724-7

Doc Ophthalmol. 2025 Apr 16. doi: 10.1007/s10633-025-10019-0. Online ahead of print.

ABSTRACT

PURPOSE: The electroretinogram (ERG) records the functional response of the retina. In some neurological conditions, the ERG waveform may be altered and could support biomarker discovery. In heterogeneous or rare populations, where either large data sets or the availability of data may be a challenge, synthetic signals with Artificial Intelligence (AI) may help to mitigate against these factors to support classification models.

METHODS: This approach was tested using a publicly available dataset of real ERGs, n = 560 (ASD) and n = 498 (Control) recorded at 9 different flash strengths from n = 18 ASD (mean age 12.2 ± 2.7 years) and n = 31 Controls (mean age 11.8 ± 3.3 years) that were augmented with synthetic waveforms, generated through a Conditional Generative Adversarial Network. Two deep learning models were used to classify the groups using either the real only or combined real and synthetic ERGs. One was a Time Series Transformer (with waveforms in their original form) and the second was a Visual Transformer model utilizing images of the wavelets derived from a Continuous Wavelet Transform of the ERGs. Model performance at classifying the groups was evaluated with Balanced Accuracy (BA) as the main outcome measure.

RESULTS: The BA improved from 0.756 to 0.879 when synthetic ERGs were included across all recordings for the training of the Time Series Transformer. This model also achieved the best performance with a BA of 0.89 using real and synthetic waveforms from a single flash strength of 0.95 log cd s m-2.

CONCLUSIONS: The improved performance of the deep learning models with synthetic waveforms supports the application of AI to improve group classification with ERG recordings.

PMID:40240677 | DOI:10.1007/s10633-025-10019-0

Eur Radiol. 2025 Apr 16. doi: 10.1007/s00330-025-11546-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the performance of high-resolution deep learning-based hip MR imaging (CSAI) compared to standard-resolution compressed sense (CS) sequences using hip arthroscopy as standard of reference.

METHODS: Thirty-two patients (mean age, 37.5 years (± 11.7), 24 men) with femoroacetabular impingement syndrome underwent 3-T MR imaging prior to hip arthroscopy. Coronal and sagittal intermediate-weighted TSE sequences with fat saturation were obtained using CS (0.6 × 0.8 mm) and high-resolution CSAI (0.3 × 0.4 mm), with 3 mm slice thickness and similar acquisition times (3:55-4:12 min). MR scans were independently assessed by three radiologists and a hip arthroscopy specialist for labral and cartilage abnormalities. Sensitivity, specificity, and accuracy were calculated using arthroscopy as reference standard. Statistical comparisons between CS and CSAI were performed using McNemar's test.

RESULTS: Labral abnormality detection showed excellent sensitivity for radiologists (CS and CSAI: 97-100%) and the surgeon (CS: 81%, CSAI: 90%, p = 0.08), with 100% specificity. Overall cartilage lesion sensitivity was significantly higher with CSAI versus CS (42% vs. 37%, p < 0.001). Highest sensitivity was observed in superolateral acetabular cartilage (CS: 81%, CSAI: 88%, p < 0.001), while highest specificity was found for the anteroinferior acetabular cartilage (CS and CSAI: 99%). Sensitivity was lowest for the assessment of the anteroinferior and posterior acetabular zones, and inferior and posterior femoral zones (CS and CSAI < 6%).

CONCLUSION: CS and CSAI MR imaging showed excellent diagnostic performance for labral abnormalities. Despite CSAI's improved cartilage lesion detection, overall diagnostic performance for cartilage assessment remained suboptimal.

KEY POINTS: Question Accurate preoperative detection of labral and cartilage lesions in femoroacetabular impingement remains challenging, with current MRI protocols showing variable diagnostic performance. Findings High-resolution deep learning-based and standard-resolution compressed sense MRI demonstrate comparable diagnostic performance, with high accuracy for labral defects but limited sensitivity for cartilage lesions. Clinical relevance Current MRI protocols, regardless of resolution optimization, show persistent limitations in cartilage evaluation, indicating the need for further technical advancement to improve diagnostic confidence in presurgical planning.

PMID:40240555 | DOI:10.1007/s00330-025-11546-9

Sci Rep. 2025 Apr 16;15(1):13188. doi: 10.1038/s41598-025-97374-9.

ABSTRACT

Jin Bei oral liquid (JBOL) is a Chinese medicinal preparation for the treatment of idiopathic pulmonary fibrosis (IPF), Clinical trials have shown that IPF patients using JBOL have improved their lung function indicators FVC% and DLCO% by approximately 2.10% and 7.74%, suggesting that the agent has a positive effect in slowing disease progression. In this study, the active volatile components of JBOL were systematically identified and analyzed using gas chromatography-mass spectrometry (GC-MS), network pharmacology and molecular docking techniques. It was found that JBOL contains a variety of compounds with antifibrotic potential, which act through multi-target and multi-pathway mechanisms. Network pharmacological analyses revealed multiple targets of JBOL associated with key pathological processes in IPF, and key active ingredients were screened based on degree values (including Sedanolide, Ligustilide, Senkyunolide H, Senkyunolide I, α-Terpineol, and 4-Terpineol). Molecular docking results showed that these compounds have high affinity for target proteins. Finally, suitable quantitative methods were established and methodologically validated for these six compounds, and these methods were used to determine the content of 8 batches of JBOL and analyze the differences in content between batches.The present study provides a scientific basis for the quality control and standardization of its JBOL by identifying and analyzing its active volatile components.

PMID:40240792 | DOI:10.1038/s41598-025-97374-9

Toxicol Appl Pharmacol. 2025 Apr 14:117342. doi: 10.1016/j.taap.2025.117342. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a debilitating lung disease characterized by excessive extracellular matrix deposition, leading to irreversible lung scarring. This study explores the underlying molecular mechanisms of IPF and delves into membrane-anchored synergism between EGFR and AnxA2, which amplifies fibrotic stress and plays a pivotal role in promoting pulmonary fibroblast activation and fibrosis. Indeed, these interactions create a synergistic effect that promotes the loss of epithelial traits and the transition to a mesenchymal phenotype, thereby contributing to fibrotic stress and disease progression. In addition, this study also explores the potential of butyrate, a short-chain fatty acid, as a therapeutic agent in reducing fibrotic stress by modulating AnxA2-EGFR signaling. Pre-treatment with butyrate significantly dampens AnxA2-EGFR signaling and Galectin-3 expression, effectively curbing prolonged EGFR phosphorylation. The suppression of upstream signaling leads to a reduction in the angiogenic marker VEGF and a decrease in pro-inflammatory mediators such as TNF-α and IL-6. Collectively, our findings highlight the critical role of EGFR-AnxA2 signaling and Galectin 3 in the pathogenesis of IPF, and highlight butyrate as a potential therapeutic agent for alleviating fibrotic stress.

PMID:40239744 | DOI:10.1016/j.taap.2025.117342