J Physiol. 2025 May 31. doi: 10.1113/JP288757. Online ahead of print.

NO ABSTRACT

PMID:40448977 | DOI:10.1113/JP288757

Mamm Genome. 2025 May 31. doi: 10.1007/s00335-025-10136-w. Online ahead of print.

ABSTRACT

Innate immunity, the primary defence mechanism, encompasses a range of protective processes like anatomical barriers, cytokine secretion, and the action of various immune cells. Cattle breeds might differ in these processes because of their genetic differences such as copy number variations (CNVs). Therefore, the present investigation employed an array comparative genomic hybridisation (aCGH) approach on breed representative pooled DNA samples to evaluate CNVs across six cattle breeds: four indigenous Indian breeds, Kangayam (KNG), Tharparkar (TP), Sahiwal (SW), Gir (GIR), one crossbred Karan Fries (KF), and one exotic breed, Holstein Friesian (HF). In aCGH, HF DNA was used as control, while test DNA was from the other breeds. Each pooled test DNA sample was a representative of 18 animals belonging to three distinct geographical locations of India. The study using Aberration Detection Method 2 (ADM-2) of Agilent Genomic Workbench revealed the highest number of duplications in KNG (1189 genes), followed by TP (534 genes), and the greatest number of deletions in SW (774 genes). Among these genes, 183 and 76 innate immune genes with hub genes TGF-β1, CD79A, and IL4 showed duplications in KNG and TP, respectively. In SW, 113 innate immune genes with hub genes PSMC5, MAPK1, and AXIN1 showed deletions. In contrast, KF and HF showed no genes with deletions and fewer duplicated innate immunity genes, reflecting either lower genetic variability in their immune gene repertoire or a potential bias due to HF DNA as a control in aCGH. Functional enrichment of innate immune genes revealed duplications in KNG enriched in interleukin-1 receptor (IL1R) activity (p = 9.9 × 10-3) and nucleobase metabolism (p = 2.88 × 10⁻11), while duplications in TP were linked to DNA-binding transcription factor activity (p = 2.34 × 10⁻14). The KEGG pathway analysis highlighted Th17 cell differentiation (p = 1.3 × 10⁻4) in KNG and Hippo signalling (p = 3.7 × 10-2) in TP. Overall, these findings highlight the importance of genetic diversity in shaping innate immunity in indigenous Indian cattle breeds, favouring a balanced crossbreeding to sustain the Indian dairy sector.

PMID:40448838 | DOI:10.1007/s00335-025-10136-w

Mol Biol Rep. 2025 May 31;52(1):529. doi: 10.1007/s11033-025-10644-7.

ABSTRACT

BACKGROUND: Multidrug-resistant and biofilm-forming pathogens have become a global health challenge, contributing to persistent and hard-to-treat infections. The objective of this study was to characterize an active metabolite produced by a novel halo-thermophilic Streptomyces sp. CBN-1 that exhibits potent antibacterial and antibiofilm activities using a combined in-silico and experimental approach.

METHODS & RESULTS: In this study, a halo-thermophilic Streptomyces sp. CBN-1 strain was selected for its ability to grow in 10% NaCl at 40 °C. This strain was identified using phenotypic characterizations and 16S rRNA gene sequence analysis as Streptomyces rochei NRRL B-2410 with 99.15% similarity. An active metabolite, CBNa-1, was extracted using n-butanol solvent from ISP2 broth medium and purified by HPLC. Structural characterization using electrospray ionization mass spectrometry and NMR spectroscopy identified CBNa-1 as an aromatic heterocyclic compound regulated by non-ribosomal peptide synthetase (NRPS) and type II polyketide synthase (PKS) genes. It exhibited potent activity with minimum inhibitory concentrations (MIC) ranging from 4 to 5 µg/mL and minimum biofilm inhibitory concentrations (MBIC50%) at ½ MIC. Additionally, in-silico docking analyses showed that CBNa-1 had stronger binding affinities from - 8.7 to -8.1 kcal/mol with isoleucyl-tRNA synthetase, glucosamine-6-phosphate synthase, penicillin-binding protein 1a, type II DNA topoisomerases, and quorum sensing compared to antibiotics (-5.7 to -7.9 kcal/mol). Furthermore, molecular dynamic (MD) simulation showed the stability of the protein-ligand complex under physiological conditions.

CONCLUSION: This study reports the first identification of CBNa-1, a metabolite from prokaryotic cells, with potent antibacterial and anti-biofilm properties to combat nosocomial infections caused by MDR pathogens, including bacteria resistant to third-generation cephalosporins.

PMID:40448741 | DOI:10.1007/s11033-025-10644-7

ISME J. 2025 May 31:wraf109. doi: 10.1093/ismejo/wraf109. Online ahead of print.

ABSTRACT

Microbial consortia play pivotal roles in nutrient cycling across diverse ecosystems, where the functionality and composition of microbial communities are shaped by metabolic interactions. Despite the critical importance of understanding these interactions, accurately mapping and manipulating microbial interaction networks to achieve specific outcomes remains challenging. Genome-scale metabolic models (GEMs) offer significant promise for predicting microbial metabolic functions from genomic data; however, traditional community GEMs typically rely on species abundance information, which may limit their predictive accuracy due to the absence of condition-specific gene expression or protein abundance data. Here, we introduce the Integration of Metatranscriptomes Into Community GEMs (IMIC) approach, which utilizes metatranscriptomic data to construct context-specific community models for predicting individual growth rates and metabolic interactions. By incorporating metatranscriptomic profiles, which reflect both gene expression activity and partially encode abundance information, IMIC could predict condition-specific flux distributions that enable the investigation of metabolite interactions among community members. Our results show that growth rates predicted by IMIC correlate strongly with relative as well as absolute abundance of species and offer a streamlined, automated procedure for estimating the single intrinsic parameter. Specifically, IMIC results in improved predictions of measured metabolite concentration changes compared with other approaches in our case study. We further demonstrate that this improvement is driven by the network-wide adjustment of flux bounds based on gene expression profiles. In conclusion, IMIC approach enables the accurate prediction of individual growth rates and improves the model performance of predicting metabolite interactions, facilitating a deeper understanding of metabolic interdependencies within microbial communities.

PMID:40448581 | DOI:10.1093/ismejo/wraf109

Vet Rec. 2025 May 30:e5325. doi: 10.1002/vetr.5325. Online ahead of print.

ABSTRACT

BACKGROUND: Bovine viral diarrhoea (BVD) is an endemic disease in the UK. In England, a voluntary control and eradication scheme, BVDFree England, has been running since 2016.

METHODS: We analysed test results from 7005 herds that were submitted to BVDFree England between 2016 and 2023 to investigate changes in the prevalence of BVD in participating herds and engagement by farmers since the previously published analysis covering the period up to 2020.

RESULTS: Herds that tested for multiple consecutive years were more likely to be BVD negative in later testing years than when starting. Few herds were still positive after 5 years of testing. Overall, the prevalence of BVD-positive herds in the dataset declined between 2020 and 2023; however, fewer farmers joined the scheme for the first time each year since 2019 (214 in 2023 compared with 2614 in 2019).

LIMITATIONS: This dataset represents the herds that submit tests to BVDFree England and is not representative of all cattle herds in England.

CONCLUSION: Herds that tested for multiple consecutive years in the scheme were less likely to be BVD positive in later years of testing, and the prevalence of BVD in participating herds has continued to fall since 2020.

PMID:40448356 | DOI:10.1002/vetr.5325

J Affect Disord. 2025 May 29:119435. doi: 10.1016/j.jad.2025.119435. Online ahead of print.

ABSTRACT

BACKGROUND: There is insufficient understanding of the long-term studies on adverse events (ADEs) in major depressive disorder (MDD) treated with atypical antipsychotics (AAPs), risks in patients with different psychiatric disorders, and differences between male and female patients.

METHODS: This study retrieved ADE reports for aripiprazole, quetiapine XR, brexpiprazole, and cariprazine from the FDA Adverse Event Reporting System (FAERS) for the time periods of FDA approval for MDD in the first quarter (Q1) of 2007, the Q1 of 2009, the Q1 of 2015, and the Q1 of 2022 respectively to the Q1 of 2024. Four algorithms (ROR, PRR, BCPNN, and MGPS) assessed ADE signals. We compared positive signal rates between MDD and non-MDD, and assessed sex differences in drug-related risks by ROR.

RESULTS: Patients with MDD had significantly higher rates of impulse control disorders (ICDs), obsessive-compulsive disorder (OCD), weight gain, extrapyramidal symptoms, and metabolic disorders compared to non-MDD (P < 0.05). Restless legs syndrome was associated with aripiprazole (P < 0.01), brexpiprazole (P < 0.01), and quetiapine XR. Serotonin syndrome, eosinophilic myocarditis, and angle closure glaucoma were new signals of aripiprazole in patients with MDD (P < 0.05). Female patients were more likely to gain weight (P < 0.05) with using aripiprazole, quetiapine XR, and brexpiprazole, whereas male patients with aripiprazole (P < 0.01) or brexpiprazole (P < 0.05) reported higher rates of ICDs and OCD.

CONCLUSION: It is suggesting a potential increased risk of various ADEs in patients with MDD when taking AAPs. The causal relationship and the exact mechanism between drugs and ADEs remains unclear, requiring further research.

PMID:40449747 | DOI:10.1016/j.jad.2025.119435

J Diabetes Sci Technol. 2025 May 30:19322968251333441. doi: 10.1177/19322968251333441. Online ahead of print.

ABSTRACT

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM's diagnostic accuracy and screening protocols. They concluded that while CGM cannot yet replace blood-based diagnostics, it holds promise as a screening tool that could lead to earlier, more effective intervention. Metrics such as time above range >140 mg/dL could indicate progression risk, and artificial intelligence (AI)-based modeling may enhance predictive capabilities. Further research is needed to establish CGM-based diagnostic criteria and refine screening strategies to improve T1D detection and intervention.

PMID:40444471 | PMC:PMC12125016 | DOI:10.1177/19322968251333441

BMC Health Serv Res. 2025 May 30;25(1):773. doi: 10.1186/s12913-025-12898-0.

ABSTRACT

The high prevalence of multimorbidity in the aging population necessitates complex medication regimens, increasing the risk of adverse drug events (ADEs) and hospital admissions. This paper evaluates an intervention aimed at improving medication safety for northeastern and western Germany under real-world conditions, thereby providing a pragmatic approach to the challenges of multi-center studies with staggered intervention starts and voluntary participation. The analysis utilizes iterative Propensity Score Matching (PSM) followed by a Difference-in-Differences (DiD) estimator to navigate the methodological complexities and assess the intervention's effectiveness and cost-effectiveness. Results reveal a significant reduction in ADE-related hospital admissions by 27.5% and overall hospital admissions by 17.5%. We find that the intervention is cost-effective at an incremental cost-effectiveness ratio (ICER) of €15,169.66 per averted ADE and €4,180.61 per averted hospital admission. This study illustrates for evaluating complex health interventions in real-world settings and underscores the importance of balancing health outcomes improvements with economic considerations in aging populations.

PMID:40448133 | DOI:10.1186/s12913-025-12898-0

Plant Methods. 2025 May 30;21(1):75. doi: 10.1186/s13007-025-01393-6.

ABSTRACT

Wheat, a major global cereal crop, is prone to quality degradation from early sprouting when stored improperly, resulting in substantial economic losses. Traditional methods for detecting early sprouting are labor-intensive and destructive, underscoring the need for rapid, non-destructive alternatives. Terahertz (THz) technology provides a promising solution due to its ability to perform non-invasive imaging of internal structures. However, current THz imaging techniques are limited by low image resolution, which restricts their practical application. We address these challenges by proposing an advanced deep learning framework for THz image classification of early sprouting wheat. We first develop an Enhanced Super-Resolution Generative Adversarial Network (AESRGAN) to improve the resolution of THz images, integrating an attention mechanism to focus on critical image regions. This model achieves a 0.76 dB improvement in Peak Signal-to-Noise Ratio (PSNR). Subsequently, we introduce the EfficientViT-based YOLO V8 classification model, incorporating a Depthwise Separable Attention (C2F-DSA) module, and further optimize the model using the Gazelle Optimization Algorithm (GOA). Experimental results demonstrate the GOA-EViTDSA-YOLO model achieves an accuracy of 97.5% and a mean Average Precision (mAP) of 0.962. The model is efficient and significantly enhances the classification of early sprouting wheat compared to other deep learning models.

PMID:40448208 | DOI:10.1186/s13007-025-01393-6

BMC Med Imaging. 2025 May 30;25(1):200. doi: 10.1186/s12880-025-01746-6.

ABSTRACT

PURPOSE: To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT).

MATERIALS AND METHODS: Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning-based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images.

RESULTS: Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0% and 83.4%, respectively (P < 0.001). The absolute percentage error in nodule volume from that of SDCT was 19.5% in ULDCT HIR-Str versus 17.9% in ULDCT DLR-Str (P < 0.001).

CONCLUSION: Compared with HIR, DLR reduced image noise, increased nodule detection rate, and improved measurement accuracy of nodule volume at chest ULDCT.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40448068 | DOI:10.1186/s12880-025-01746-6

Sci Rep. 2025 May 30;15(1):19103. doi: 10.1038/s41598-025-04061-w.

ABSTRACT

The Internet of Vehicles (IoV) is crucial in facilitating secure and efficient vehicle-infrastructure communication. Nevertheless, with an increasing reliance on the IoV in modern logistics and intelligent fleet systems, cyberattacks on vital supply chain information pose a far greater threat. This research presents the ASCON, a low-power cryptographic algorithm, with the Message Queued Telemetry Transport (MQTT) protocol for secure IoV communications. Integration of a deep learning model that is suited for real-time anomaly detection and breach prediction. The novelty of this study is the hybrid framework that uses lightweight cryptographic methods coupled with deep learning-based threat protection. Therefore, it is resilient against a wide range of cyber-attacks, including password cracking, authentication compromises, brute-force attacks, differential cryptanalysis, and Zig-Zag attacks. The system employs Raspberry Pi boards with authentic industrial vehicluar dataset and offers a remarkable encryption rate of 0.025 s, takes 0.003 s for hash generation, and detection of tampering takes 0.002 s. By bridging the gap between high-level cryptography and proactive and smart security analytics, this work not only fortifies fleet management systems but also makes substantial contributions to the overall objectives of enhancing safety, sustainability, and operational robustness in autonomous vehicle networks.

PMID:40447743 | DOI:10.1038/s41598-025-04061-w

Commun Biol. 2025 May 30;8(1):836. doi: 10.1038/s42003-025-08184-8.

ABSTRACT

Multiplexed imaging techniques require identifying different cell types in the tissue. To utilize their potential for cellular and molecular analysis, high throughput and accurate analytical approaches are needed in parsing vast amounts of data, particularly in clinical settings. Nuclear segmentation errors propagate in all downstream steps of cell phenotyping and single-cell spatial analyses. Here, we benchmark and compare the nuclear segmentation tools commonly used in multiplexed immunofluorescence data by evaluating their performance across 7 tissue types encompassing ~20,000 labeled nuclei from human tissue samples. Pre-trained deep learning models outperform classical nuclear segmentation algorithms. Overall, Mesmer is recommended as it exhibits the highest nuclear segmentation accuracy with 0.67 F1-score at an IoU threshold of 0.5 on our composite dataset. Pre-trained StarDist model is recommended in case of limited computational resources, providing ~12x run time improvement with CPU compute and ~4x improvement with the GPU compute over Mesmer, but it struggles in dense nuclear regions.

PMID:40447729 | DOI:10.1038/s42003-025-08184-8

Spectrochim Acta A Mol Biomol Spectrosc. 2025 May 27;342:126487. doi: 10.1016/j.saa.2025.126487. Online ahead of print.

ABSTRACT

Drug repositioning and reuse is a cost-effective strategy for the development of new drugs, and drug co-crystal is a fast and effective technical means. Acetylsalicylic acid is a BCS II drug, which has the limitations of high permeability and low solubility, and the safety and efficacy of the drug have been greatly affected. Co-crystallization with other forming agents is considered to be a promising technical means, which can not only increase the solubility, but also improve the dissolution rate and stability. In this paper, the cocrystal of acetylsalicylic acid and arginine was prepared by grinding method. The physical and chemical characterization of the raw material, the mixture and the obtained cocrystal was carried out by XRD, terahertz spectroscopy (THz-TDS) and Raman spectroscopy (Raman). The obvious difference was observed on the characteristic peaks of the cocrystal, which proved the formation of the cocrystal. Understanding the basic properties of lattice vibration during the eutectic process is challenging, yet it can be accomplished through theoretical calculations. By employing density-functional theory (DFT) calculations, the molecular configurations and vibration spectra of the two drug cocrystals can be obtained, enabling a deeper understanding of the vibration modes of drug molecules in the low-frequency range. Moreover, this study demonstrates the sensitivity of terahertz time-domain spectroscopy (TDS) technology in detecting intermolecular hydrogen-bond interactions in drug cocrystals. When comparing cocrystal molecules with active pharmaceutical ingredient (API) molecules, it is found that cocrystals possess better binding energy, driven by intermolecular hydrogen bonds and dispersion forces.

PMID:40446719 | DOI:10.1016/j.saa.2025.126487

EBioMedicine. 2025 May 29;117:105779. doi: 10.1016/j.ebiom.2025.105779. Online ahead of print.

ABSTRACT

BACKGROUND: Motor neuron disease (MND) leads to progressive functional decline, making reliable measures of disease progression critical for patient care and clinical trials. Current clinical outcome measures lack the ability to continuously and objectively track functional decline in daily life of patients with MND. This study assessed and validated wrist-worn accelerometry outcome measures for continuous monitoring in MND, with the potential to refine clinical trial outcomes.

METHODS: This longitudinal study included 95 patients with MND who wore an ActiGraph GT9X Link device on their non-dominant wrist for 8 days, with follow-up every 3-4 months. Accelerometer data were processed using ActiLife and GGIR. Joint models were used to simultaneously investigate the longitudinal change in ALS Functional Rating Scale-Revised (ALSFRS-R) scores and accelerometer-derived outcomes alongside their relationship with overall survival. Sample size estimates for clinical trials were generated using both accelerometer- and ALSFRS-R-based outcomes, and principal component analysis (PCA) explored outcome relationships.

FINDINGS: Accelerometer outcomes showed a slower rate of decline (-0.03 to -0.07 SD/month) compared to ALSFRS-R (-0.10 SD/month) and had stronger correlations with ALSFRS-R motor subdomains (partial r: 0.60-0.73). PCA revealed that longitudinal measures of accelerometry were distinct from the ALSFRS-R, highlighting the complementary nature of these measures. Peak 6-min activity predicted smaller clinical trial sample sizes for studies over 12 months. Accelerometer-derived outcomes were not significantly associated with survival.

INTERPRETATION: Wrist-worn accelerometry offers a practical solution for continuous monitoring in MND, complementing ALSFRS-R. Measures of peak performance, and specifically peak 6-min activity shows promise, potentially reducing sample sizes and improving disease tracking over longer duration studies. Further refinement and validation are needed to adopt actigraphy measures as clinical assessment outcomes.

FUNDING: This study was supported by Wesley Medical Research (2016-32), the Honda Foundation, Motor Neurone Disease Research Australia, and FightMND. CJH received a Higher Degree Research Scholarship from UQ. STN received support from the Scott Sullivan Fellowship (MND and Me Foundation/RBWH Foundation), a FightMND Mid-Career Fellowship, and the AIBN.

PMID:40446399 | DOI:10.1016/j.ebiom.2025.105779

Anim Microbiome. 2025 May 30;7(1):53. doi: 10.1186/s42523-025-00405-z.

ABSTRACT

Several studies have indicated that the dysregulation of microbial metabolites and the inflammatory environment resulting from microbial dysbiosis may contribute to the occurrence and progression of cardiovascular diseases. Therefore, restoring the disordered gut microbiota in patients with colorectal cancer by fecal microbiota transplantation (FMT) has the potential to reduce the incidence of cardiac disease. In this study, we identified cardiac dysfunction in azomethane and dextran sodium sulfate-induced colorectal cancer mice. Intestinal microbes from healthy mice were transferred to colorectal cancer mice, which vastly reversed the disorder of the gut microbiota and effectively alleviated cardiac dysfunction. Moreover, FMT regulated the expression of serum metabolites such as uridine triphosphate (UTP), tiamulin, andrographolide, and N-Acetyl-D-glucosamine, as well as cytokines like TGF-β, IRF5, and β-MHC in the heart. These findings uncover that the disturbed gut microbiota causes cardiac dysfunction in colorectal cancer mice by modulating the expression of serum metabolites and cytokines, which could be alleviated by treatment with FMT.

PMID:40448218 | DOI:10.1186/s42523-025-00405-z

NPJ Antimicrob Resist. 2025 May 30;3(1):46. doi: 10.1038/s44259-025-00115-1.

ABSTRACT

The lack of novel antimicrobial compounds in the development pipeline cries for innovative approaches regarding their discovery. In this Perspective, we discuss how microbial interactions play a significant role in shifting a pathogen's response to antibacterial treatment and negatively impact patient outcomes. Furthermore, we argue that interspecies interactions are often overlooked in treatment selection and current drug screening approaches, and modeling disease-relevant polymicrobial communities could help in unraveling novel strategies to eradicate pathogens.

PMID:40447766 | DOI:10.1038/s44259-025-00115-1

Thorax. 2025 May 30:thorax-2024-222242. doi: 10.1136/thorax-2024-222242. Online ahead of print.

ABSTRACT

BACKGROUND: Despite significant clinical improvements, there is evidence of persisting airway inflammation in people with cystic fibrosis (CF) established on elexacaftor/tezacaftor/ivacaftor (ETI) therapy. As CF is a multi-system disease, systemic immune profiles can reflect local inflammation within the lungs and other organs. Understanding systemic inflammation after ETI therapy may reveal important translational insights. This study aims to profile systemic inflammatory changes and relate these to the well-documented improvements observed with ETI therapy.

METHODS: We conducted a single-centre longitudinal study with 57 CF subjects initiating ETI therapy. All participants were Phe508del homozygous or Phe508del/minimal function. Blood samples were collected pre-ETI and 3-12 months post-therapy initiation. Analyses included mass spectrometry-based proteomics, a multiplex immunoassay, and flow cytometry for peripheral immune cell counts and phenotype. Controls samples were provided by 29 age-matched healthy controls.

RESULTS: Systemic inflammation reduced with ETI therapy; however, the immune profile remained distinct from healthy controls. ETI reduced neutrophil counts and was associated with a more mature, less inflammatory phenotype, as well as a shift towards an immune resolving state associated with increased CD206 expression. Cytokines known to influence neutrophil levels reduced with therapy. Despite ETI therapy, neutrophil and monocyte counts remained elevated compared with healthy controls. There was no obvious association between the ETI-related improvements in systemic inflammation and lung function.

CONCLUSIONS: Patients with CF showed evidence of persisting systemic inflammation despite ETI therapy, which may have long-term potentially adverse effects on respiratory and other organ systems.

PMID:40447326 | DOI:10.1136/thorax-2024-222242

Int J Biol Macromol. 2025 May 28:144741. doi: 10.1016/j.ijbiomac.2025.144741. Online ahead of print.

ABSTRACT

Polymeric hydrogels are paramount to outstanding applications in biology, medicine, pharmacy. Their similarity to living tissues is leveraged in clinical branches (oncology, cardiology, immunology, neurology, wound healing) for delivering a large range of drugs (encompassing DNA, RNA, protein molecules) and realizing in-vivo models of stimuli-responsive or controlled drug release. Rubber elasticity theory and the swollen network hypothesis are key for properly designing the geometric and mechanical features of hydrogels and polymer networks. The assumption of a Gaussian distribution of end-to-end lengths in a polymer molecule, however, can break down in several cases. Here, strongly supported by Low field NMR and rheology experiments, we propound the generalized Weibull law of extreme value statistics (EVS) to have universal validity in hydrogel materials. Mesh size values that account for an intrinsic statistical dependence between monomeric positions (or stiffness) show much better agreement with measurements conducted on physically crosslinked samples (agar, alginate and scleroglucan), including sputum specimens (rich in mucins) from patients affected by chronic respiratory conditions (cystic fibrosis) and on chemically crosslinked samples (poly-vinylpyrrolidone, PVP; poly-(ethylene-glycol/propylene-glycol), PEG/PPG). Across all ten gels, the Gaussian distribution yields the smallest average mesh size, ranging roughly from 7 nm for the densest alginate 2 % (9 gl-1) hydrogel to about 80 nm for one of the sputum. Working with the pierced Gaussian inflates the mesh size to ≈1.5 × the Gaussian value, with increases from a modest +4 % in alginate 1 % up to nearly +100 % in the open PVP network (48 → 98 nm). The generalized Weibull distribution usually falls between the two Gaussians, yet in agar 1 % and scleroglucan 2 % it overtakes the pierced Gaussian (e.g. 20.2 > 15.8 nm for agar 1 %), reflecting a strong heavy-tailed distribution. The predicted mesh order therefore is Gaussian < generalized Weibull ≈ pierced Gaussian, with the precise ranking ruled by the width and skewness of each network statistics. Overall, our findings - being straightforward to apply - will profoundly impact on the description, conception and control of polymer networks, which often demand advanced instrumental techniques for compensating the lack of adequate predictive models. Among other relevant implications, aside from drug delivery, we highlight the characterization of the photothermal (or thermoplasmonic) response of hydrogel matrices hosting metal nanoparticles (e.g. with applications in hyperthermia cancer treatment and enhanced chemical processes). On the theoretical side, we emphasize the study of transport and thermomechanical properties of polymeric networks.

PMID:40446991 | DOI:10.1016/j.ijbiomac.2025.144741

BMC Med Inform Decis Mak. 2025 May 30;25(1):200. doi: 10.1186/s12911-025-03029-0.

ABSTRACT

PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

MATERIALS AND METHODS: A retrospective analysis was conducted using data from 1,251 patients in the BraTS2021 dataset as the main cohort and 181 clinical cases collected from a medical center between April 2013 and June 2018 (51 years ± 17; 104 males) as the external test set. We propose a PatchGAN-based modality imputation network with an Aggregated Residual Transformer (ART) module combining Transformer self-attention and CNN feature extraction via residual links, paired with a U-Net variant for segmentation. Generative accuracy used PSNR and SSIM for modality conversions, while segmentation performance was measured with DSC and HD95 across necrotic core (NCR), edema (ED), and enhancing tumor (ET) regions. Senior radiologists conducted a comprehensive Likert-based assessment, with diagnostic accuracy evaluated by AUC. Statistical analysis was performed using the Wilcoxon signed-rank test and the DeLong test.

RESULTS: The best source-target modality pairs for imputation were T1 to T1ce and T1ce to T2 (p < 0.001). In subregion segmentation, the overall DSC was 0.878 and HD95 was 19.491, with the ET region showing the highest segmentation accuracy (DSC: 0.877, HD95: 12.149). Clinical validation revealed an improvement in grading accuracy by the senior radiologist, with the AUC increasing from 0.718 to 0.913 (P < 0.001) when using the combined imputation and segmentation models.

CONCLUSION: The proposed deep learning framework improves high-grade glioma grading by modality imputation and segmentation, aiding the senior radiologist and offering potential to advance clinical decision-making.

PMID:40448035 | DOI:10.1186/s12911-025-03029-0

BMC Med Imaging. 2025 May 30;25(1):197. doi: 10.1186/s12880-025-01737-7.

ABSTRACT

OBJECTIVE: This study aimed to construct a novel model, Multi-Spatial Attention U-Net (MSAU-Net) by incorporating our proposed Multi-Spatial Attention (MSA) block into the U-Net for the automated segmentation of the gallbladder on CT images.

METHODS: The gallbladder dataset consists of CT images of retrospectively-collected 152 liver cancer patients and corresponding ground truth delineated by experienced physicians. Our proposed MSAU-Net model was transformed into two versions V1(with one Multi-Scale Feature Extraction and Fusion (MSFEF) module in each MSA block) and V2 (with two parallel MSEFE modules in each MSA blcok). The performances of V1 and V2 were evaluated and compared with four other derivatives of U-Net or state-of-the-art models quantitatively using seven commonly-used metrics, and qualitatively by comparison against experienced physicians' assessment.

RESULTS: MSAU-Net V1 and V2 models both outperformed the comparative models across most quantitative metrics with better segmentation accuracy and boundary delineation. The optimal number of MSA was three for V1 and two for V2. Qualitative evaluations confirmed that they produced results closer to physicians' annotations. External validation revealed that MSAU-Net V2 exhibited better generalization capability.

CONCLUSION: The MSAU-Net V1 and V2 both exhibited outstanding performance in gallbladder segmentation, demonstrating strong potential for clinical application. The MSA block enhances spatial information capture, improving the model's ability to segment small and complex structures with greater precision. These advantages position the MSAU-Net V1 and V2 as valuable tools for broader clinical adoption.

PMID:40448013 | DOI:10.1186/s12880-025-01737-7