Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models

OBJECTIVES

To explore the optimal choice of seven diffusion models (DWI, IVIM, DKI, CTRW, FROC, SEM, and sADC) to assess renal interstitial fibrosis (IF) and annual renal function loss in chronic kidney disease (CKD).

METHODS

One hundred thirty-three CKD patients and 30 controls underwent multi-b diffusion sequence scans. Patients were divided into the training, testing, and temporal external validation sets. Least absolute shrinkage and selection operator regression and logistic regression were used to select the optimal metrics for distinguishing the mild from moderate-to-severe IF. The performances of imaging, clinical, and combined models were compared. A linear mixed-effects model calculated estimated glomerular filtration rate (eGFR) slope, and multiple linear regression assessed the association between metrics and 1-3-year eGFR slopes.

RESULTS

The training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical fIVIM, MKDKI and eGFR was superior to the clinical model combining the eGFR and 24-hour urinary protein in all sets (net reclassification index [NRI] > 0, p < 0.05). Decision curve analysis showed the combined model provided greater net clinical benefit across most thresholds. Fifty-two, 35, and 16 patients completed 1-, 2-, and 3-year follow-ups. After adjusting for covariates, cortical fIVIM correlated with the 1-year eGFR slope (β = 30.600, p = 0.001), and cortical αSEM correlated with the 2- and 3-year eGFR slopes (β = 44.859, p = 0.002; β = 95.631, p = 0.019).

CONCLUSIONS

A combined model of cortical fIVIM, MKDKI and eGFR provides a useful comprehensive tool for grading IF, with cortical fIVIM and αSEM as potential biomarkers for CKD progression.

Advances in therapies using mesenchymal stem cells and their exosomes for treatment of peripheral nerve injury: state of the art and future perspectives

"Peripheral nerve injury" refers to damage or trauma affecting nerves outside the brain and spinal cord. Peripheral nerve injury results in movements or sensation impairments, and represents a serious public health problem. Although severed peripheral nerves have been effectively joined and various therapies have been offered, recovery of sensory or motor functions remains limited, and efficacious therapies for complete repair of a nerve injury remain elusive. The emerging field of mesenchymal stem cells and their exosome-based therapies hold promise for enhancing nerve regeneration and function. Mesenchymal stem cells, as large living cells responsive to the environment, secrete various factors and exosomes. The latter are nano-sized extracellular vesicles containing bioactive molecules such as proteins, microRNA, and messenger RNA derived from parent mesenchymal stem cells. Exosomes have pivotal roles in cell-to-cell communication and nervous tissue function, offering solutions to changes associated with cell-based therapies. Despite ongoing investigations, mesenchymal stem cells and mesenchymal stem cell-derived exosome-based therapies are in the exploratory stage. A comprehensive review of the latest preclinical experiments and clinical trials is essential for deep understanding of therapeutic strategies and for facilitating clinical translation. This review initially explores current investigations of mesenchymal stem cells and mesenchymal stem cell-derived exosomes in peripheral nerve injury, exploring the underlying mechanisms. Subsequently, it provides an overview of the current status of mesenchymal stem cell and exosome-based therapies in clinical trials, followed by a comparative analysis of therapies utilizing mesenchymal stem cells and exosomes. Finally, the review addresses the limitations and challenges associated with use of mesenchymal stem cell-derived exosomes, offering potential solutions and guiding future directions.

Advances in biosensors for diagnosis of Alzheimer's and Parkinson's diseases

Early diagnosis by detecting ultralow concentrations of disease biomarkers is critical for timely treatment of the two most common neurodegenerative diseases, Alzheimer's and Parkinson's diseases. Innovative biosensors technologies can provide accurate, faster, and cheaper diagnostic pathways. In this review, the most recent electrochemical and optical sensing and biosensing platforms for diagnosing these diseases are critically selected and reviewed. Diagnostic targets (generally biomarkers) related to each disease and novel technologies, such as nanomaterials and biomolecular techniques to optimize the detection process and enhance signals, are discussed. In particular, multiplex detection and detection of multiple analytes by a (bio) sensing platform, to improve clinical sensitivity and selectivity are considered. This review is intended to open new approaches in the field and advance future research by identifying those strategies that optimize real-world performance and minimize present shortcomings.

A catalase-like nanozyme of high activity and stability in acidic solutions for enzyme immobilization and chemoenzymatic cascade conversion of glucose to gluconic acid

Gluconic acid is widely used in food and pharmaceutical. However, its bio-synthesis by glucose oxidase (GOx) is blocked by depletion of dissolved O2, reduction of pH value, and accumulation of hydrogen peroxide (H2O2). To remove the by-products and replenish O2, developing a nanozyme with high catalase (CAT)-like activity and stability under acidic conditions is a prescription. Herein, we developed a manganese-based nanozyme (ps-MnOx-BSA) through a stepwise strategy and encapsulated it in Fe-doped zeolitic imidazolate framework-8 (FZ). The as-prepared ps-MnOx-BSA@FZ (MFZ) exhibited not only high stability and CAT-like activity but also exceptionally no peroxidase-like activity at pH 5.5. Thereafter, GOx@MFZ was fabricated by the immobilization of GOx on MFZ and utilized for gluconic acid synthesis with a yield of 98.3 % within 30 min. GOx@MFZ retained 92.3 % of its initial activity after six batches. This work provided a novel strategy for the design of acid-resistant CAT-like nanozyme toward sustainable gluconic acid production.

Achievements and challenges in glucose oxidase-instructed multimodal synergistic antibacterial applications

Glucose oxidase (GOx) with unique catalytic properties and inherent biocompatibility can effectively oxidize both endogenous and exogenous glucose with oxygen (O2) into gluconic acid and hydrogen peroxide (H2O2). Accordingly, the GOx-based catalytic chemistry offers new possibilities for designing and constructing multimodal synergistic antibacterial systems. The consumption of glucose permanently downregulates bacterial cell metabolism by blocking essential energy supplies, inhibiting their growth and survival. Additionally, the production of gluconic acid could downregulates the pH within the bacterial infection microenvironment, enhancing the production of hydroxyl radicals (∙OH) from H2O2 via enhanced Fenton or Fendon-like reactions and triggering the pH-responsive release of drugs. Furthermore, the generated H2O2 in situ avoids the addition of exogenous hydrogen peroxide. Therefore, it is possible to design GOx-based multimodal antibacterial synergistic therapies by combining GOx-instructed cascade reactions with other therapeutic approaches such as chemodynamic therapies (CDT), hypoxia-activated prodrugs, photosensitizers, and stimuli-responsive drug release. Such multimodal strategies are expected to exhibit better therapeutic effects than single therapeutic modes. This tutorial review highlights recent advancements in GOx-instructed multimodal synergistic antibacterial systems, focusing on design philosophy and construction strategies. Current challenges and future prospects for advancing GOx-based multimodal antibacterial synergistic therapies are discussed.

Recent advances in biomimetic nanodelivery systems for the treatment of glioblastoma

Glioblastoma remain one of the deadliest malignant tumors in the central nervous system, largely due to their aggressiveness, high degree of heterogeneity, and the protective barrier of the blood-brain barrier (BBB). Conventional therapies including surgery, chemotherapy and radiotherapy often fail to improve patient prognosis due to limited drug penetration and non-specific toxicity. We then present recent advances in biomimetic nanodelivery systems, focusing on cell membrane coatings, nanoenzymes, and exosome-based carriers. By mimicking endogenous biological functions, these systems demonstrate improved immune evasion, enhanced BBB traversal, and selective drug release within the tumor microenvironment. Nevertheless, we acknowledge unresolved bottlenecks related to large-scale production, stability, and the intricacies of regulatory compliance. Looking forward, we propose an interdisciplinary roadmap that combines materials engineering, cellular biology, and clinical expertise. Through this collaborative approach, this work aims to optimize biomimetic nanodelivery for glioma therapy and ultimately improve patient outcomes.

Promotion of quiescence and maintenance of function of mesenchymal stem cells on substrates with surface potential

The widespread use of human mesenchymal stem cells(hMSCs) is impeded by functional loss during prolonged expansion. Although multiple approaches have been attempted to preserve hMSCs stemness, a suitable culture system remains to be modified. The interaction between electrical signals and stem cells is expected to better maintain the function of stem cells. However, it remains unclear whether the surface potential of substrates has the potential to preserve stem cell function during in vitro expansion. In our study, hMSCs cultured on materials with different surface potentials could be induced into a reversible quiescent state, and we demonstrated that quiescent hMSCs could be reactivated and transitioned back into the proliferation cell cycle. hMSCs cultured under appropriate potential displayed superior differentiation and proliferation abilities within the same generation compared to conventional conditions. These findings underscore the importance of surface potential as a critical physical factor regulating hMSCs stemness. Manipulating the surface potential of hMSCs culture substrates holds promise for optimising preservation and culture conditions, thereby enhancing their application in tissue repair and regeneration engineering.

Pre-ligand-induced porous MOF as a peroxidase mimic for electrochemical analysis of deoxynivalenol (DON)

Developing convenient and sensitive vomitoxin detection methods is crucial to prevent human health risks from excess deoxynivalenol (DON) in food products. This study synthesized porous electrochemical nanomaterial calcined PA-NH2-MIL-101 (CPNM) with abundant amino group modifications using a palmitic acid (PA) pre-ligand and amino functionalization scheme. PA-induced defect generation and which formed a high-stability porous structure that increased the peroxidase-like catalytic active site and thus improving electrochemical analytical performance. In addition, introducing amino groups in CPNM facilitated the covalent immobilization of DON antibodies. Therefore, an electrochemical immunosensing platform for detecting DON was developed by utilizing the electrocatalytic signals generated by Fe-MOF (MIL-101) nanozymes and thionine molecules. The proposed sensor showed a large linear range of 10-107 pg mL-1 with a detection limit of 9.6 pg mL-1 (S/N = 3) under optimized optimal conditions. Consequently, this innovative electrochemical immunosensing technique based on CPNM nanozymes paves the way for DON detection in food.

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

Myeloid cells in the microenvironment of brain metastases

Brain metastasis (BrM) from peripheral solid tumors has a high mortality rate and remains a daunting clinical challenge. In addition to the targeting of tumor cells, studies have focused on the regulation of the tumor microenvironment (TME) for BrM treatment. Here, through a review of recent studies, we revealed that myeloid infiltration is a common feature of the TME in BrMs from different primary sites even though the brain is regarded as an immune-privileged site and is always in an immunosuppressive state. Tumor-educated bone marrow progenitors, especially mesenchymal stem cells (MSCs), may impact the brain tropism and and phenotypic switching of myeloid cells. Additionally, chronic inflammation may be key factors regulating the immunosuppressive TME and myeloid cell reprogramming. Here, the role of myeloid cells in the formation of the TME and strategies for targeting these cells before and after BrM are reviewed, emphasizing the potential for the use of myeloid cells in BrM treatment. However, the direct relationship between the neuronal system and myeloid cell filtration is still unclear and worthy of further study.

FKBP5 promotes osteogenic differentiation of mesenchymal stem cells through type-I interferon pathway Inhibition

The decreased osteogenesis of bone marrow mesenchymal stem cells (BMSCs) is an important factor causing bone loss. Nevertheless, its deep molecular mechanism has still not been fully clarified. To elucidate the regulatory mechanisms underlying BMSC osteogenesis, we conducted a bioinformatics screen using public datasets from the Gene Expression Omnibus (GEO) database to identify genes displaying significant expression dynamics during the osteogenic differentiation of BMSCs. We observed a significant upregulation of FK506 Binding Protein 5 (FKBP5) expression during the osteogenic differentiation of BMSCs. Besides, knockdown and overexpression of FKBP5 could reduce and increase osteogenic markers and Alizarin Red S (ARS) staining, respectively. Enrichment analysis of RNA sequencing (RNA-seq) demonstrated that downregulation of FKBP5 activated IFNα/β signaling pathway. FKBP5 overexpression relieved the inhibitory effect of IFNβ on osteogenesis. In addition, one of the upregulated interferon-stimulated genes (ISG), interferon-induced protein with tetratricopeptide repeats 2 (IFIT2), negatively regulated osteogenesis of BMSCs. IFIT2 knockdown rescued negative effect on osteogenesis caused by downregulation of FKBP5. Hydroxyapatite scaffold implanted in nude mice and drilled tibiae model in C57BL/6 mice confirmed positive role of FKBP5 in osteogenesis in vivo. Therefore, we determined the beneficial effect of FKBP5 on osteogenesis of BMSCs and validated the critical role of FKBP5/IFIT2 axis in this process. These findings might contribute to comprehension and treatment of bone diseases, like osteoporosis and bone fracture.

Robust Hydrogen-Bonded Organic Framework for pH-Responsive Drug Delivery

Hydrogen-bonded organic frameworks (HOFs), by virtue of their low toxicity, wide substrate tolerance, porosity, and regenerative and biocompatible traits, are an emerging class of porous polymeric materials that show great potential for intracellular delivery of chemotherapeutics. However, stability issues coupled with controlled release of a drug under desired stimuli have restricted their therapeutic potential. In the present study, based on density functional theory calculations predicting strong noncovalent interactions between the H-bonded 4,4',4″-(1,3,5-triazine-2,4,6-triyl)tribenzoic acid (H3TATB) dimer and the chemotherapeutic drug doxorubicin (Dox), we prepared a robust and reticular nanoplatform Dox@nano-HOF 1, which released Dox at low pH, making it an ideal carrier for targeted drug delivery to tumor sites sparing the normal tissues. Additionally, the cytotoxic and apoptosis assays confirmed the efficacy of the nanocarrier in inducing dose-dependent cell death in cancer cells. This study opens up new and hitherto unexplored avenues for the use of robust HOFs in pH-responsive delivery of anticancer therapeutics.

Comparative analysis of convolutional neural networks and vision transformers in identifying benign and malignant breast lesions

Various deep learning models have been developed and employed for medical image classification. This study conducted comprehensive experiments on 12 models, aiming to establish reliable benchmarks for research on breast dynamic contrast-enhanced magnetic resonance imaging image classification. Twelve deep learning models were systematically compared by analyzing variations in 4 key hyperparameters: optimizer (Op), learning rate, batch size (BS), and data augmentation. The evaluation criteria encompassed a comprehensive set of metrics including accuracy (Ac), loss value, precision, recall rate, F1-score, and area under the receiver operating characteristic curve. Furthermore, the training times and model parameter counts were assessed for holistic performance comparison. Adjustments in the BS within Adam Op had a minimal impact on Ac in the convolutional neural network models. However, altering the Op and learning rate while maintaining the same BS significantly affected the Ac. The ResNet152 network model exhibited the lowest Ac. Both the recall rate and area under the receiver operating characteristic curve for the ResNet152 and Vision transformer-base (ViT) models were inferior compared to the others. Data augmentation unexpectedly reduced the Ac of ResNet50, ResNet152, VGG16, VGG19, and ViT models. The VGG16 model boasted the shortest training duration, whereas the ViT model, before data augmentation, had the longest training time and smallest model weight. The ResNet152 and ViT models were not well suited for image classification tasks involving small breast dynamic contrast-enhanced magnetic resonance imaging datasets. Although data augmentation is typically beneficial, its application should be approached cautiously. These findings provide important insights to inform and refine future research in this domain.

Diagnostic Value of 99mTc-Ubiquicidin Scintigraphy in Differentiating Bacterial from Nonbacterial Pneumonia

Purpose: Differentiating purely viral from bacterial etiologies continues to be a challenging yet key step in the management of community-acquired pneumonia (CAP), further highlighted since the COVID-19 pandemic. This study aims to evaluate the utility of 99mTc-ubiquicidin (UBI) in the differentiation of bacterial from nonbacterial pneumonia. Methods: A total of 30 patients with CAP were allocated into groups A, bacterial (n = 15), and B, viral pneumonia (n = 15). All patients underwent 99mTc-UBI scan with planar and single-photon emission computed tomography (SPECT) images of thorax acquired at 30 and 180 min postinjection. Target-to-background (T/B) ratios were calculated with values >1.4 interpreted as positive for bacterial infection. Correlation was made with computed tomography (CT) scan and polymerase chain reaction (PCR) results. Results: UBI scan was positive in 43.3% (n = 13) of patients, with sensitivity, specificity, and accuracy of 86.7%, 100%, and 93.3%, respectively, and close correlation with chest CT scan and PCR results (p-value = 0.000). Planar images were generally not helpful. Receiver operating characteristic curve analysis indicated similar diagnostic performance for 30-min and 3-h SPECT images by implementing T/B thresholds of 1.2 and 1.33, respectively. Conclusions: 99mTc-UBI SPECT is a promising modality for differentiating purely viral from bacterial or superimposed bacterial pneumonia and provides reliable evidence either to mandate or withhold administration of antibiotics in patients with CAP.

Assessment of lymph node metastases in patients with ovarian high-grade serous carcinoma: Incremental diagnostic value of dual-energy CT combined with morphologic parameters

OBJECTIVE

To explore the feasibility of Dual-Energy Computed Tomography (DECT) in distinguishing metastatic from non-metastatic lymph nodes (LNs) in ovarian High-Grade Serous Carcinoma (HGSC), and to assess the incremental diagnostic value of combining DECT with morphologic parameters in differentiating metastatic and non-metastatic LNs.

METHODS

From October 2021 to May 2024, 141 LNs from 39 patients with HGSC who underwent DECT were retrospectively enrolled. LNs were matched with the pathological report. Five morphologic parameters and nine DECT parameters were assessed. DECT parameters were obtained from both the arterial and venous phases, including the attenuation at 40 and 70 keV, slope of the spectral Hounsfield unit curve (λHu), Virtual Non-Contrast (VNC), Iodine Concentration (IC), Normalized Iodine Concentration (NIC), electron density (Rho), effective atomic number (Zeff) and Dual-Energy Index (DEI). Independent-sample Student's t test was used to compare continuous variables, while multivariable binary logistic regression analyses was applied to identify independent predictors for LN metastasis in the morphology, DECT, and combined models. Receiver Operating Characteristic (ROC) analysis was performed to evaluate the diagnostic performance of these three models in differentiating metastatic from non-metastatic LNs.

RESULTS

86 metastatic LNs and 55 non-metastatic LNs were finally enrolled in our study. The short diameter (S), long diameter (L), and S/L ratio were significantly larger in metastatic LNs compared to non-metastatic LNs (9.69 ± 4.06 vs. 6.37 ± 1.24 mm, P < 0.001; 13.99 ± 5.36 vs.9.61 ± 2.30 mm, P < 0.001; 0.70 ± 0.15 vs. 0.67 ± 0.12, P = 0.023). In the venous phase, λHU, VNC and Rho were significantly higher in metastatic LNs compared to non-metastatic LNs (-3.596 ± 1.115 vs. -4.234 ± 1.077, P = 0.001; 24.242 ± 9.867 vs. 15.826 ± 11.830, P < 0.001; 32.557 ± 8.023 vs. 26.936 ± 9.420, P < 0.001), while IC, NIC, Zeff, DEI were significant lower in metastatic LNs than non-metastatic LNs (1.872 ± 0.678 vs. 2.404 ± 1.140, P = 0.001; 38.309 ± 14.443 vs. 47.247 ± 22.270, P = 0.005; 8.513 ± 0.320 vs. 8.719 ± 0.360, P = 0.001; 0.014 ± 0.006 vs. 0.018 ± 0.007, P = 0.045). The Area Under the Curve (AUC) of morphology model and DECT model were 0.793 (95 %CI: 0.721-0.862) and 0.762(95 %CI: 0.690-0.825), respectively. The combination of the morphology model and DECT model revealed optimal diagnostic performance (AUC = 0.845; 95 %CI: 0.780-0.896), which was significantly higher than that of the individual models (P = 0.015, P = 0.006, respectively).

CONCLUSION

DECT parameters provide incremental diagnostic value in assessing metastatic LNs in patients with HGSC. The combination of the morphology and DECT models significantly improves diagnostic performance compared to the standalone morphology model.

Alterations in genomic features and the tumour immune microenvironment predict immunotherapy outcomes in advanced biliary tract cancer patients

BACKGROUND

The response to immunotherapy is limited in advanced biliary tract cancer (BTC). Response prediction is a serious challenge in the clinic.

METHODS

This study included 60 patients with advanced BTC who received anti-PD-1 treatment. Among these patients, 30 were subjected to 520 gene panel sequencing, and 50 were subjected to multiplex circulating cytokine testing. The entropy and mutation features were analysed via the optimized pipeline based on our previous work. The repeated LASSO algorithm was used to identify the optimal features. The associations between sequence features and cell communications were explored by analysing single-cell transcriptome data from BTC (GSE125449). Cox regression was used to develop the integrated model. Time-dependent C-index, Kaplan‒Meier, and receiver operating characteristic (ROC) curves were used to assess the prediction performance.

RESULTS

TP53, NRAS, FBXW7, and APC were identified as prognosis-related genes. The average C-indices of sequence entropy (0.819) and mutation (0.817) for overall survival (OS) were significantly greater than those of tumour mutation burden (TMB, 0.392) and mutation score (0.638). Single-cell transcriptome data revealed that TP53, KRAS, and NRAS were enriched in plasmacytoid dendritic cells (pDCs) and that the communication between pDCs and macrophages was mediated through the CXCL signalling pathway. The integrated model (EM-CXCL10) showed powerful predictive performance for survival status (AUC: 0.863, 95% CI: 0.643-0.972) and objective response rate (AUC: 0.990, 95% CI: 0.822-1.000).

CONCLUSIONS

This study constructed a multidimensional strategy that might indicate the prognosis of BTC immunotherapy, enabling the recognition of BTC patients who would benefit from immunotherapy, thereby guiding personalized clinical decision-making.

Multimodal magnetic resonance imaging studies on non-motor symptoms of Parkinson's disease

Objective

This study aims to investigate the diagnostic value of multi-modal magnetic resonance imaging (MRI) utilizing arterial spin labeling (ASL), quantitative susceptibility mapping (QSM), and 3D T1-weighted imaging (3DT1WI) in patients with Parkinson's disease (PD). Additionally, it evaluates the relationship between MRI findings and non-motor symptoms associated with PD.

Methods

ASL, QSM, and 3DT1WI scans were performed on 48 PD patients and 46 healthy controls (HC). We extracted and analyzed differences in regional cerebral blood flow (rCBF), magnetic susceptibility, and gray matter density parameters between the two groups. These MRI parameters were correlated with clinical scale scores assessing non-motor symptoms, including cognitive function, sleep quality, olfaction, autonomic function, anxiety, depression, and fatigue. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of each imaging modality in distinguishing PD from HC.

Results

The areas under the ROC curve (AUC) for rCBF, magnetic susceptibility, and gray matter density were 0.941, 0.979, and 0.624, respectively. In PD patients, a negative correlation was found between Unified Parkinson's Disease Rating Scale Part II (UPDRS II) scores and rCBF in the bilateral precuneus. The Pittsburgh Sleep Quality Index (PSQI) scores negatively correlated with rCBF in the left middle temporal gyrus and right middle occipital gyrus. Hamilton Depression Rating Scale (HAMD) scores positively correlated with QSM values in the right supplementary motor area, while scores on the Argentine Smell Identification Test (AHRS) negatively correlated with QSM values in the same area. Disease duration showed a positive correlation with QSM values in the right middle cingulate gyrus. Additionally, PSQI scores positively correlated with QSM values in the left middle cingulate gyrus, and fatigue severity scale (FSS) scores also positively correlated with QSM values in the left middle cingulate gyrus. Gray matter atrophy in the left inferior temporal gyrus was associated with cognitive impairment in PD.

Conclusion

Occipital hypoperfusion and cortical atrophy in the left inferior temporal gyrus may serve as novel imaging biomarkers for PD and are associated with sleep disturbances and cognitive impairment in PD patients. Extensive iron deposition in the bilateral cerebral cortex of PD patients may be a contributing factor to non-motor symptoms such as sleep disturbances and fatigue. Multimodal imaging techniques, including ASL, QSM, and 3DT1WI, can enhance the diagnostic accuracy for PD.

Cardiac magnetic resonance left atrioventricular coupling index as a prognostic tool in hypertrophic cardiomyopathy

AIMS

A novel marker left atrioventricular coupling index (LACI) has been proved to be associated with cardiovascular events in patients without history of cardiovascular disease. However, the studies on cardiac magnetic resonance-derived LACI in hypertrophic cardiomyopathy (HCM) patients are limited, and the prognostic value of LACI has still not been studied thoroughly, so we aimed to explore the association between LACI and adverse clinical outcomes in HCM patients.

METHODS

A total of 206 HCM patients underwent cardiac magnetic resonance examination were retrospectively enrolled. LACI is defined by the ratio between the left atrial (LA) volume and the left ventricular (LV) volume in LV end-diastolic phase. The composite endpoint was categorized into death-related, heart failure-related, and arrhythmia-related events, reflecting mortality risk, heart failure progression, and arrhythmia burden, respectively. Receiver operating characteristics curve analysis was used to determine the optimal cut-off value for LACI to distinguish HCM patients at high risk of adverse clinical outcome. Multivariable Cox regression models were built including significant clinical variables, LA ejection fraction (LAEF), LA volume index (LAVI), late gadolinium enhancement (LGE) extent and LACI. The improvement of discrimination by adding LACI to a clinical model was assessed using C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

Thirty-four HCM patients reached the endpoint during a median follow-up time of 60 [interquartile range (50-68)] months. In the multivariate Cox regression analysis, LACI [hazard ratio 1.054, 95% confidence interval (CI): 1.037, 1.071; P < 0.001] was an independent predictor of the composite events after adjustment for age and atrial fibrillation. Then 40.09% was identified as an optimal cut-off for LACI in the risk stratification. Integrating LACI to the clinical model yielded higher C-statistic 0.892 with 95% CI (0.861, 0.922) compared with LA diameter, LAEF, LAVI and LGE extent, providing an improvement in prediction of high-risk patients (NRI = 0.627, 95% CI: 0.112-0.934; IDI = 0.295, 95% CI: 0.016-0.709).

CONCLUSIONS

LACI is an independent risk factor for clinical adverse outcome and is superior to conventional LA parameters and LGE extent for the identification of high-risk HCM patients.

Nomogram using bi-modal imaging for predicting low-level HER2 expression status in breast cancer

OBJECTIVE

To develop and validate a nomogram based on ultrasound and mammographic imaging features for predicting human epidermal growth factor receptor 2-low (HER2-low) expression status in breast cancer.

METHODS

Patients with HER2-negative breast cancer (n = 316) were retrospectively recruited and randomized into training (n = 221) and validation (n = 95) cohorts. Patients were categorized into HER-low and HER2-zero expression groups. Ultrasound and mammography images were collected. Univariate and multivariate analyses were used to identify independent risk factors for HER-low expression status in the training cohort. A predictive nomogram model was developed and validated in the validation cohort. The calibration, discrimination, and clinical net benefit of the nomogram model were assessed using calibration curves, receiver operating characteristic curves, and decision curve analyses, respectively. A Kaplan-Meier curve was drawn, and the log-rank test was used to compare progression-free survival of the two groups of patients.

RESULTS

A longer diameter, tumor margin that was not circumscribed in ultrasound and mammography images, posterior acoustic shadowing, and a higher maximum elasticity were independent predictors of HER2-low expression status; thus, they were incorporated into the nomogram model. The area under the receiver operating characteristic curve (AUC) of the nomogram model was 0.783 in the training cohort. The nomogram also showed good discrimination in the validation cohort (AUC = 0.810), and good calibration efficiency in both cohorts. Decision curve analysis indicated that the nomogram was clinically useful. The log-rank test result revealed a significant difference in progression-free survival. HER2-low expression correlated with improved breast cancer prognosis.

CONCLUSION

This nomogram may provide reference for selecting candidates for appropriate management.

Enhanced the Trans-Cleavage Activity of CRISPR-Cas12a Using Metal-Organic Frameworks as Stimulants for Efficient Electrochemical Sensing of Circulating Tumor DNA

Continued development of clustered regularly interspaced short palindromic repeats (CRISPR)-powered biosensing system on the electrochemical interface is vital for accurate and timely diagnosis in clinical practice. Herein, an electrochemical biosensor based on manganese metal-organic frameworks (MOFs)-enhanced CRISPR (MME-CRISPR) is proposed that enables the efficient detection of circulating tumor DNA (ctDNA). In this design, customized enzyme stimulants (Mn2+) are co-assembled with Cas12a/crRNA to form enzyme-MOF composites, which can be released quickly under mild conditions. The MOFs-induced proximity effect can continuously provide adequate Mn2+ to sufficiently interact with Cas12a/crRNA during the release process, enhancing the trans-cleavage activity of complex available for biosensor construction. The MOFs-based enzyme biocomposites also afford efficient protection against various external stimulus. It is demonstrated that the developed biosensor can achieve ultrasensitive detection of epidermal growth factor receptor L858R mutation in ctDNA with a low detection limit of 0.28 fm without pre-amplification. Furthermore, the engineered mismatch crRNA enables the biosensor based on MME-CRISPR to detect single nucleotide variant with a high signal-to-noise ratio. More importantly, it has been successfully used to detect the targets in clinical practice, requiring low-dose samples and a short time. This strategy is believed to shed new light on the applications of cancer diagnosis, treatment, and surveillance.

Detection of hypochlorous acid fluctuation via a near-infrared fluorescent probe in Parkinson's disease cells and mouse models

Parkinson's disease (PD) is a neurodegenerative disorder caused by excessive reactive halogen species leading to the death of dopaminergic (DA) neurons, which disrupts the coordination of normal physiological structures and functions. Hypochlorous acid (HOCl) is a reactive halogen species whose overproduction is associated with the death of DA neurons. Herein, overproduction of HOCl may be a neurotoxin substance in the pathogenesis of PD. Therefore, it is essential to understand the disease of HOCl in PD model. However, early detection HOCl in PD model remains lacking of effective methods. In this study, a high sensitivity off-on near-infrared probe (MB-HOCl) was designed and synthesized. MB-HOCl showed a quantitative response toward HOCl (0-100 μM) with detection limit of 0.32 μM. Importantly, MB-HOCl was capable of selectively and specially detecting exogenous and endogenous HOCl in PC-12 cells and was successfully used for imaging in PD mice models. All results demonstrate that the probe (MB-HOCl) holds great promise for understanding the disease and diagnosis of HOCl-mediated PD models.

Machine learning unravels the mysteries of glioma typing and treatment

Gliomas, which are complex primary malignant brain tumors known for their heterogeneous and invasive nature, present substantial challenges for both treatment and prognosis. Recent advancements in whole-genome studies have opened new avenues for investigating glioma mechanisms and therapies. Through single-cell analysis, we identified a specific cluster of cancer cell-related genes within gliomas. By leveraging diverse datasets and employing non-negative matrix factorization (NMF), we developed a glioma subtyping method grounded in this identified gene set. Our exploration delved into the clinical implications and underlying regulatory frameworks of the newly defined subtype classification, revealing its intimate ties to glioma malignancy and prognostic outcomes. Comparative assessments between the identified subtypes revealed differences in clinical features, immune modulation, and the tumor microenvironment (TME). Using tools such as the limma R package, weighted gene co-expression network analysis (WGCNA), machine learning methodologies, survival analyses, and protein-protein interaction (PPI) networks, we identified key driver genes influencing subtype differentiation while quantifying associated outcomes. This study not only sheds light on the biological mechanisms within gliomas but also paves the way for precise molecular targeted therapies within this intricate disease landscape.

Urolithins: Emerging natural compound targeting castration-resistant prostate cancer (CRPC)

Castration-resistant prostate cancer (CRPC) presents a significant challenge due to its resistance to conventional androgen deprivation therapies. Urolithins, bioactive metabolites derived from ellagitannins, have recently emerged as promising therapeutic agents for CRPC. Urolithins not only inhibit androgen receptor (AR) signaling, a crucial factor in the progression of CRPC, but also play a key role in regulating oxidative stress by their antioxidant properties, thereby inhibiting increased reactive oxygen species, a common feature of the aggressive nature of CRPC. Research has shown that urolithins induce apoptosis and diminish pro-survival signaling, leading to tumor inhibition. This review delves into the intricate mechanisms through which urolithins exert their therapeutic effects, focusing on both AR-dependent and AR-independent pathways. It also explores the exciting potential of combining urolithins with androgen ablation therapy, opening new avenues for CRPC treatment.

Accuracy of MRI R2* Mapping in Diagnosis of Liver Iron Concentration: A Meta-Analysis

While current guidelines recommend R2* method as the first-line method for liver iron concentration (LIC) measurement, its diagnostic accuracy is debatable. A prior meta-analysis suggested limited accuracy of R2* method for identifying patients with iron overload. However, substantial advances in R2* method over the past decade may have improved its diagnostic performance. The purpose of this study is to explore the accuracy of the R2* method in identifying patients at clinically relevant LIC thresholds (1.8, 3.2, 7.0, and 15.0 mg/g). This is a meta-analysis. On February 16, 2024, databases including PubMed, Medline, Embase, Web of Science, and the Cochrane Library were searched for studies using the R2* method to quantify LIC. R2* values were derived from gradient echo sequence, with LIC from the FerriScan® (R2) or biopsy as reference standard. The true positive, false positive, true negative, and false negative at each LIC threshold (1.8, 3.2, 7.0, and 15.0 mg/g) were extracted from each study by two reviewers. Summary receiver operating characteristic (SROC) curves were generated using the hierarchical SROC (HSROC) model. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Twenty-six studies with 1687 patients were included. In identifying patients at each LIC threshold (1.8, 3.2, 7.0, and 15.0 mg/g), the area under the SROC curves (AUCs) were 0.96 or higher, with pooled sensitivities of 0.95 or higher. Pooled specificities were 0.93 or higher; however, studies using biopsy as a reference showed a wide range 95% confidence interval (CI) (0.51-0.99) for identifying patients at the 1.8 mg/g threshold. The R2* method accurately identified patients at different clinically relevant LIC thresholds (1.8, 3.2, 7.0, and 15.0 mg/g). However, when biopsy is used as a reference, the method may be unstable for the 1.8 mg/g threshold (95% CI for pooled specificity, 0.51-0.99). LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Photothermal composites for in-situ trace oil detection on ultra-clean surfaces

Oil is widely used as a lubricant in many industries. However, in the final product, oil is often considered a contaminant. In various production processes, such as semiconductor manufacturing, precision machining, and optical device fabrication, ultra-clean surfaces are crucial. Oil residues on these surfaces can adversely affect product performance and quality. Detecting and controlling oil residues enables enhancing product reliability and consistency. Previous lipid detection studies necessitate complex pre-processing, large instruments, or specialized manipulation and data reading capabilities. Existing methods for trace oil detection still cannot achieve on-site real-time monitoring. This study presents a photothermal conversion-based platform for trace oil adsorption and detection. Using polyester fiber membranes as the substrate, a composite material of chitosan and MXene was modified to remove trace oil on ultra-clean surfaces. Due to their inherent thermal conductivity and ability to trap heat inside, the oil molecules intensify the material's temperature rise under near-infrared excitation at 808 nm. By connecting a thermal imaging module to a smartphone, real-time detection of trace oil can be achieved.

Inert-remodeling strategy to build bimetal-confined nitrogen-doped carbon nanozyme for colorimetric-chemiluminescent imaging dual-mode cascade enzyme sensing

The metal-organic frameworks (MOFs)-derived nanozymes in air atmosphere have gained great attention in biosensing fields. Nevertheless, this derivative pattern may result in the destabilization of the MOF framework and the aggregation of active sites, consequently diminishing its catalytic activity. Herein, we reported an inert-remodeling strategy to build bimetal-confined nitrogen-doped carbon nanozyme for dual-mode cascade enzyme biosensing. The strategy was easily achieved by pyrolysis of MOFs (CoNi-ZIF-67 as model) precursor in argon atmosphere, leading to the formation of CoNi bimetallic nanoparticles uniformly confined nitrogen-doped carbon (CoNi-CN) nanozyme. This derivative nanozyme exhibits significantly enhanced peroxidase (POD)-like activity, which is 4 times higher than that of NiCo2O4 nanozyme (CoNi-ZIF-67 derivative in air atmosphere) and 54 times higher than that of CoNi-ZIF-67 precursor. The excellent POD-like activity of CoNi-CN nanozyme is ascribed to the following facts: i) integrate structure with uniformly dispersed CoNi bimetal active sites; ii) confinement effect of CoNi bimetal encapsulated in CN architecture. Integrating with glucose oxidase (GOx) to prepare cascade enzyme of CoNi-CN@GOx, colorimetric-chemiluminescent imaging sensor based on CoNi-CN@GOx cascade system was developed for glucose detection. Glucose was assayed in wide linear ranges of 0.08-15 mM (colorimetric) and 0.1-30 mM (CL imaging). This research provides a promising inert-remodeling strategy to construct high-performance nanozyme for dual mode biosensing applications.

Cobalt MOF-hybridized nanozyme catalysts breaking pH limitations for boosted chlorpyrifos sensing performance

Given the potential dangers of organophosphorus pesticides to food safety and human health, the development of a reliable and precise detection platform for pesticides is essential. In this study, we present a novel 'armor-plating' laccase-mimetic catalyst (DNA-Cu@MOFs)-based colorimetric platform, which enables stable and selective pesticide detection. The DNA-Cu@MOFs enhance catalytic stability and overcome pH limitations, enabling effective catalysis under neutral and alkaline physiological conditions, making them well-suited for practical applications in biosensor development. By combining the catalytic properties of DNA-Cu@MOFs with a high-affinity biorecognition element (acetylcholinesterase), the platform achieves a linear detection range of 3.0-90 ng mL-1 for chlorpyrifos, with a detection limit of 0.75 ng mL-1. Notably, this platform demonstrates significant stability in chlorpyrifos detection even in the presence of environmental interferents. This robust colorimetric platform offers new possibilities for pesticide detection and provides a solid foundation for the development of comprehensive and accurate pesticide monitoring systems.

Anti-biopassivated Reticular Micromotors for Bladder Cancer Therapy

The limited lifespan of enzyme-powered micro/nanomotors (MNMs) hinders their biomedical applications due to the easy deactivation in tumor microenvironments. In this study, by taking advantage of hydrogen bond-rich metal-organic frameworks (MOFs), we design anti-biopassivated urease-powered MOF motors (Ur-MOFtors) with sustained motility for bladder cancer therapy. Such reticular Ur-MOFtors exhibited an exceptionally long locomotion lifespan exceeding 90 min in highly concentrated urea, which was an 18-fold enhancement compared with urease-adsorbed MOFs, resulting in excellent anti-biopassivation of MOFtors. The underlying molecular mechanism of persistent motion involves hydrogen bonding interaction between the MOF skeleton and the catalytic product, as identified by in situ infrared spectroscopy and density functional theory. Based on the preserved enzymatic activity comparable to native urease, the self-propulsion pathway of Ur-MOFtors is driven by ionic self-diffusiophoresis with the positive chemotactic motion toward urea. Benefiting from the persistent motion of Ur-MOFtors in physiological urea, a rapid bladder cancer therapy was achieved with few instillation sessions and short treatment cycles during intravesical administration. This hydrogen bond-rich framework presents a promising anti-biopassivated approach to overcoming the short lifespan and easy deactivation of enzymatic motors for advanced therapeutic robotics.

Dual-Energy CT-Based Assessment of Thrombotic Heterogeneity for Predicting Stroke Source and Response to Machine Thrombectomy: A Step Toward Visualization Thrombus Treatment

The viability of using thrombus heterogeneity (TH) data derived from dual-energy CT (DECT) as a visual thrombotic biomarker is unclear. The first aim of this study is to develop a quantitative measure of TH on DECT and test its performance for predicting the stroke source (cardiogenic vs. non-cardiogenic) and clinical outcomes (functional status assessed by the modified Rankin Scale score at 90 days) following machine thrombectomy (MT). The second aim is to associate thrombus subregions with the thrombus composition to facilitate visualization of thrombus constituents. Radiomics data are extracted from the whole thrombus and subregions in CT/DECT to construct predictive models. The performances of all models are evaluated and compared in the validation and comparative cohorts. Histopathologic analysis is performed to correlate the subregion data with the actual thrombus composition. This study included 221 and 255 participants who underwent DECT and CT examinations, respectively. DECT outperformed CT in predicting stroke source and clinical outcomes, with the TH-related models showing the highest performance in the validation and comparative cohorts. Thrombus composition is correlated with the different CT/DECT-based subregions, with DECT-habitat_c showing the strongest association. Thrombus subregion analyses may help visualize the related constituents.

China Protocol for early screening, precise diagnosis, and individualized treatment of lung cancer

Early screening, diagnosis, and treatment of lung cancer are pivotal in clinical practice since the tumor stage remains the most dominant factor that affects patient survival. Previous initiatives have tried to develop new tools for decision-making of lung cancer. In this study, we proposed the China Protocol, a complete workflow of lung cancer tailored to the Chinese population, which is implemented by steps including early screening by evaluation of risk factors and three-dimensional thin-layer image reconstruction technique for low-dose computed tomography (Tre-LDCT), accurate diagnosis via artificial intelligence (AI) and novel biomarkers, and individualized treatment through non-invasive molecule visualization strategies. The application of this protocol has improved the early diagnosis and 5-year survival rates of lung cancer in China. The proportion of early-stage (stage I) lung cancer has increased from 46.3% to 65.6%, along with a 5-year survival rate of 90.4%. Moreover, especially for stage IA1 lung cancer, the diagnosis rate has improved from 16% to 27.9%; meanwhile, the 5-year survival rate of this group achieved 97.5%. Thus, here we defined stage IA1 lung cancer, which cohort benefits significantly from early diagnosis and treatment, as the "ultra-early stage lung cancer", aiming to provide an intuitive description for more precise management and survival improvement. In the future, we will promote our findings to multicenter remote areas through medical alliances and mobile health services with the desire to move forward the diagnosis and treatment of lung cancer.

DNA binding effects of LDH nanozyme for aseptic osteolysis mitigation through STING pathway modulation

Persistent and intense inflammation is recognized as the primary cause of wear-particle-induced aseptic osteolysis, which ultimately resulting in aseptic prosthesis loosening. Reducing inflammation plays a significant role in mitigating osteolysis, and the STING pathway has emerged as a promising therapeutic target for its prevention. Specifically, damaged periprosthetic cells of aseptic osteolysis release double-stranded DNA (dsDNA) into the osteolytic microenvironment, serving as a specific stimulus for the STING pathway. Herein, we found that layered double hydroxide (LDH) nanozyme exhibited a robust DNA-binding capacity primarily mediated by van der Waals interactions, which showed superior performance in inhibiting dsDNA-induced inflammation of aseptic osteolysis. Importantly, such binding capability enabled effective co-loading LDH with STING inhibitor C176, thus facilitating inhibition of the STING pathway. Such synergistic actions contributed to ameliorate the inflammatory milieu and remodel the osteolysis microenvironment successfully to reduce cranial bone damage, which was confirmed on animal model of osteolysis. Collectively, this strategy demonstrated an effective approach by utilizing synergistic effects to establish a positive feedback loop in the treatment of osteolysis, thereby alleviating TiPs-induced periprosthetic osteolysis and preventing postoperative complications.

Bioinspired rational design of nanozymes

Nanozymes, an emerging class of artificial enzymes, have attracted increasing attention for their potential in environmental monitoring, industrial catalysis, food safety, and biomedicine. To date, more than 1500 nanomaterials have been identified with enzyme-like activities, some demonstrating catalytic performances that match or even exceed those of natural enzymes. Despite this progress, key challenges remain, including poorly understood catalytic mechanisms, ambiguous structure-activity relationships, and a heavy dependence on nonspecific surface sites, all of which limit the efficiency, selectivity, and broader application of nanozymes. To address these limitations, researchers are turning to nature for inspiration, seeking to reconstruct enzyme active centers at the atomic scale and establish innovative design principles. This review examines the catalytic mechanisms and structural characteristics of natural enzymes, integrating machine learning approaches to investigate nanozyme kinetics, transition state stabilization, electron/proton transfer, and cooperative effects. It highlights bioinspired strategies such as three-dimensional structure design, cofactor incorporation, and artificial organelle systems. Furthermore, the review explores rational nanozyme design using activity descriptors and predictive modeling. Finally, it outlines the transformative potential of artificial intelligence and multiscale simulations in optimizing nanozyme performance, offering a theoretical foundation for the development of next-generation intelligent nanozymes.

Magnetic nanosheets: from iron oxide nanocubes to polydopamine embedded 2D clusters and their multi-purpose properties

We here develop stable bidimensional magnetic nanoclusters (2D-MNCs) of iron oxide nanocubes (IONCs) arranged in thin nanosheets of closed-packed nanocubes. The assembly occurs by means of a two-step approach: in the first one, the ionic surfactant, sodium dodecyl sulfate (SDS), acts as a transient water transfer agent and as 2D clustering agent to induce formation of a monolayer of nanocubes arranged in thin nanosheets. Next, the addition of dopamine followed by solution basification, induces the in situ polymerization of dopamine with a tunable shell tickness depending on the dopamine amount, which helps to compact the clusters and ensures the long term water stability of the clusters. TEM, cryo-EM, and SAXS techniques helped to reveal structural features of the 2D-clusters. The pH-dependent degradation properties of polydopamine, enable to disassemble the clusters in acidic tumour microenviroment leading to a four-fold increase in the magnetic particle imaging signal and a concomitant increase of the magnetic heat losses of these clusters, makes them appealing in magnetic hyperthermia, while the shortening of T2 relaxation time suggests their use as contrast in magnetic resonance imaging. Finally, with crystal violet dye, used as drug molecule, the feasibility to release payloads pre-encapsulated with the polydopamine polymer shell has been also shown.

Screening Anti-Parkinson's Disease Drugs in Living Mouse Brains via a Peroxynitrite-Activated Fluorescent Probe

Screening anti-Parkinson's disease (PD) drugs at in vivo brain level is imperative for managing PD yet currently remains unaccomplished. Peroxynitrite (ONOO-) has been implicated in PD progression. Thus, developing in vivo ONOO--based imaging tools for anti-PD drug screening holds promise for early prognosis and treatment of PD. Consequently, a near-infrared (NIR) fluorescence probe, BOB-Cl-PN, with high specificity, good sensitivity (LOD = 24 nM), and rapid response (<60 s), was devised to investigate ONOO- and PD relationships. Utilizing NIR fluorescence imaging, BOB-Cl-PN effectively monitored ONOO- fluctuations in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD cell models, establishing a cellular high throughput screening (cHTS) system for anti-PD drugs. In live animal imaging, BOB-Cl-PN's ability to penetrate the blood-brain barrier enabled ONOO- flux imaging of PD mouse brains. Moreover, BOB-Cl-PN served as an imaging contrast for in vivo screening of potential traditional Chinese medicines for PD therapy, identifying fisetin as having the best therapeutic index among 10 Chinese medicines. This study constructs a sensitive, efficient imaging contrast for monitoring ONOO- dynamics in PD brains and provides a valuable platform for cellular and in vivo screening of anti-PD drugs.

Multimodal Diffusion MRI Synergized with VI-RADS for Precision Grading of Bladder Urothelial Carcinoma: A Prospective Diagnostic Model Validation

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), neurite orientation dispersion and density imaging (NODDI), and the Vesical Imaging-Reporting and Data System (VI-RADS) in discriminating the pathological grade of bladder urothelial carcinoma (UCB).

MATERIALS AND METHODS

This prospective study enrolled patients with pathologically confirmed UCB between May 2023 and December 2023. Preoperative MRI protocols included spin-echo echo-planner imaging (SE-EPI) and conventional DWI. Quantitative parameters from SE-EPI (DTI, DKI, MAP, NODDI) and apparent diffusion coefficient (ADC) values were measured. Group comparisons between low-grade and high-grade UCB were performed using t-tests or Mann-Whitney U tests. Receiver operating characteristic (ROC) analysis and DeLong's test were used to evaluate diagnostic performance.

RESULTS

A total of 50 patients with UCB (low-grade/ high-grade = 16/34) were included. VI-RADS score and mean kurtosis (MK) derived from DKI emerged as independent predictors for differentiating low-grade and high-grade UCB (area under the curve (AUC): 0.692 and 0.865, respectively). The combination of VI-RADS and DKI-MK achieved superior diagnostic performance (AUC: 0.915, sensitivity: 0.941) compared to VI-RADS alone (AUC: 0.692, sensitivity: 0.471; p < 0.001) or ADC alone (AUC: 0.787, sensitivity: 0.813; p < 0.05).

CONCLUSION

Integrating VI-RADS with DKI-MK significantly enhances preoperative assessment of UCB pathological grading, demonstrating higher accuracy and sensitivity than VI-RADS or ADC alone. This approach improves diagnostic objectivity by combining qualitative imaging criteria with quantitative diffusion metrics, offering potential clinical utility for treatment stratification.

MRI-based habitat analysis for Intratumoral heterogeneity quantification combined with deep learning for HER2 status prediction in breast cancer

BACKGROUND

Human epidermal growth factor receptor 2 (HER2) is a crucial determinant of breast cancer prognosis and treatment options. The study aimed to establish an MRI-based habitat model to quantify intratumoral heterogeneity (ITH) and evaluate its potential in predicting HER2 expression status.

METHODS

Data from 340 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed. Two tasks were designed for this study: Task 1 distinguished between HER2-positive and HER2-negative breast cancer. Task 2 distinguished between HER2-low and HER2-zero breast cancer. We developed the ITH, deep learning (DL), and radiomics signatures based on the features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Clinical independent predictors were determined by multivariable logistic regression. Finally, a combined model was constructed by integrating the clinical independent predictors, ITH signature, and DL signature. The area under the receiver operating characteristic curve (AUC) served as the standard for assessing the performance of models.

RESULTS

In task 1, the ITH signature performed well in the training set (AUC = 0.855) and the validation set (AUC = 0.842). In task 2, the AUCs of the ITH signature were 0.844 and 0.840, respectively, which still showed good prediction performance. In the validation sets of both tasks, the combined model exhibited the best prediction performance, with AUCs of 0.912 and 0.917 respectively, making it the optimal model.

CONCLUSION

A combined model integrating clinical independent predictors, ITH signature, and DL signature can predict HER2 expression status preoperatively and noninvasively.

Metal-organic frameworks: a biomimetic odyssey in cancer theranostics

This review comprehensively explores biomimetic metal-organic frameworks (MOFs) and their significant applications in cancer theranostics. Although MOFs have promising features such as adjustable porosity, improved surface area, and multifunctionality, they are limited by factors like low biocompatibility and specificity. Biomimetic strategies involving biological membranes and materials are proposed to address these challenges. The review begins by examining the unique characteristics and preparation methods of biomimetic carriers used in MOF-based nanoplatforms, with a comparative analysis of each method. It then delves into the various biomedical applications of biomimetic MOFs, including biosensing, bioimaging, immunotherapy, gene therapy, and multimodal therapies. The review also discusses the bio-interaction of these nanoplatforms, including their immunogenicity and interactions with fluids and tissues. Toxicity perspectives are also critically assessed. Overall, the article emphasizes the need for continued research into biomimetic MOFs, highlighting their potential to overcome current obstacles and provide safe, effective, and targeted therapeutic options.

Ca2+ transfer via enhancing ER-Mito coupling contributed to BDE-47- induced hippocampal neuronal necroptosis and cognitive dysfunction

2,2,4,4-Tetrabromodiphenyl ether (BDE-47), a ubiquitous environmental pollutant, has gained increasing attention due to its high level in biological samples and potential neurotoxicity. Recent studies have indicated that the receptor interacting protein kinase 1 (RIPK1)-mediated necroptosis is implicated in BDE-47 cytotoxicity. However, little is known about the underlying mechanism and whether the necroptosis participates in BDE-47-induced neuronal injury and cognitive impairment. Our results indicated that exposure to BDE-47 triggered RIPK1-dependent neuronal necroptosis in mice hippocampi and HT-22 mouse hippocampal neurons. Necrostain-1 (Nec-1), a specific RIPK1 inhibitor, suppressed the RIPK1/RIPK3/mixed lineage kinase-like domain protein (MLKL) signaling and rescued neuronal survival in BDE-47-treated HT-22 neurons. Mechanically, increased mitochondrial Ca2+ influx precipitated the opening of the mitochondrial permeability transition pore (mPTP), leading to occurrence of hippocampal neuronal necroptosis under BDE-47 stress. BDE-47 exposure induced excessive mitochondria-associated endoplasmic reticulum membranes (MAMs) formation and promoted ER-to-mitochondria Ca2+ transfer, while diminishing ER-mitochondrial contacts by Glucose-regulated protein 75 (Grp75)-deficiency remarkably prevented mitochondria Ca2+ overload and opening of mPTP as well as neuronal necroptosis. Notably, Nec-1 pre-treatment could substantially mitigate neuronal/synaptic damage and cognitive impairment in BDE-47-exposed mice. Collectively, these data suggest that BDE-47 exposure intensified endoplasmic reticulum (ER)-mitochondrial (Mito) contact and facilitated Ca2+ transfer from ER towards mitochondria, resulting in mPTP opening-mediated hippocampal neuronal necroptosis and subsequent cognitive dysfunction. Our study shed new light on the mechanisms underlying BDE-47 neurotoxicity and provided a novel therapeutic strategy through targeting RIPK1 kinase activity.

Spatial Configuration-Guided Design of Covalent Organic Framework-Based Artificial Metalloantioxidases for Inhibiting Inflammatory Cascades and Regulating Bone Homeostasis

Intense oxidative stress in bone tissues can trigger the hyperactivation of neutrophils, thereby causing inflammatory cascades to deteriorate bone homeostasis. Here, inspired by the catalytic centers of natural antioxidases, we introduce the spatial configuration-guided design of covalent organic framework (COF)-based artificial metalloantioxidases for inhibiting inflammatory cascades and regulating bone homeostasis. Specifically, the hexaiminohexaazatrinaphthalene COF with ruthenium coordination (S-HACOF-Ru), featuring electron-rich centers with a spatial configuration, demonstrates exceptional antioxidase-like reactive oxygen species (ROS) scavenging capabilities for efficiently mitigating the oxidative stress. As a result, S-HACOF-Ru efficiently prevents the generation of neutrophil extracellular traps and inhibits the release of myeloperoxidase (MPO). By preventing MPO-induced activation of nuclear factor-kappa B and inhibiting proinflammatory macrophage polarization, S-HACOF-Ru successfully blocks the neutrophil-macrophage inflammatory cascades. This intervention promotes bone homeostasis by a shift from bone resorption to tissue regeneration, which can efficiently inhibit alveolar bone loss in periodontal tissues and reverse cartilage damage in ankle joint cavities. We propose that this design strategy provides an intriguing avenue for developing new artificial antioxidases and biocatalytic materials with potential applications in treating a wide range of chronic inflammatory diseases.

Hierarchically Macroporous Ce-MOF Nanozyme with Enhanced Phosphoester Hydrolase- and Oxidase-like Activities for Self-Cascade Colorimetric Detection of Profenofos On-Site

The extensive application of profenofos (PFF), a widely used organophosphorus pesticide (OP), has raised significant environmental and health concerns due to its accumulation in ecosystems and its inhibitory effects on acetylcholinesterase in humans. Despite advancements in analytical technologies, currently available chromatography and electrochemical assays often involve complex procedures, high costs, and specialized equipment, limiting their applicability for routine and on-site PFF monitoring. Here, we report a novel self-cascade nanozyme-based colorimetric biosensor employing a hierarchically macroporous Ce-MOF (HMUiO-66(Ce)) with integrated phosphoester hydrolase (PEH)- and oxidase (OXD)-like activities. The HMUiO-66(Ce) nanozyme features hierarchical macrochannels that enhance mass transfer and substrate accessibility, significantly improving its cascade sensing performance compared with conventional UiO-66(Ce). Through PEH-OXD cascade catalysis, PFF is hydrolyzed into 4-bromo-2-chlorophenol, which undergoes selective oxidative coupling via OXD-like catalysis, yielding a distinct red-colored product. This colorimetric response is highly specific to PFF, as other organophosphates do not trigger the OXD-catalyzed coupling, minimizing interference and ensuring high analytical selectivity. The colorimetric biosensor can be seamlessly integrated with a smartphone for on-site detection, exhibiting a broad linear detection range (0.1-50 μg/mL) and an impressively low detection limit (0.068 μg/mL), surpassing most existing colorimetric methods. This work provides new insights into the development of a highly sensitive and selective biosensor through the self-cascade principle, offering great potential for on-site screening of environmental pollutants.

Lightweight triangular mesh deformable reconstruction for low quality 3D organ models: Thickness noise and uneven topology

Lightweight triangular mesh models have great potential for real-time 3D visualization of lesions during minimally invasive surgery (MIS). However, the blurred tissue boundaries, high imaging noise, and unoriented points in medical images seriously affect the accuracy and topological quality of surface reconstruction, which can lead to inaccurate lesion localization. In this paper, we present a robust and high-topology-quality triangular mesh reconstruction method that aims to provide a deformable expression model for real-time 3D visualization during surgery. Our approach begins by approximating the model prototype under the guidance of an unsigned distance field by simulating inflation. Then, we introduce a variance-controlled cylindrical domain projection search (VC-CDPS) method to achieve the final surface fitting. Additionally, we incorporate topology optimization into the iterative reconstruction process to ensure smoothness and good topology of the reconstruction model. To validate our method, we conduct experiments on a geometric model with high noise and a human organ model manually segmented by novice doctors. The results demonstrate that our reconstructed model exhibits better surface quality and noise immunity. Furthermore, we conduct a comparison experiment of model deformation and propose a metric to measure the topological quality of the model. Through in vitro tissue experiments, we explored the relationship between topological quality and deformation accuracy. The results reveal a positive correlation between deformation accuracy and topological quality.

Triphenylmethyl-based carbon biradical nanoparticles for magnetic resonance imaging

In recent years, luminescent carbon radicals have emerged as a promising functionalized material, with a primary focus on harnessing the potential of mono-radicals. The greater spin density exhibited by a single molecule in biradicals undoubtedly provides greater research value for its magnetic properties. Conventional metallic contrast agents represent the predominant choice in magnetic resonance imaging (MRI) of clinical applications. However, the accumulation of metal ions within the body poses potential safety risks. Hence, the exploration of innovative metal-free organic magnetic nanomaterials offers a safer alternative. This work introduces an innovative approach, presenting the first metal-free carbon-radical-based MRI contrast agent by encapsulating luminescent triphenylmethyl biradicals into nanoparticles (TTM-PhTTM NPs). These biradical NPs exhibit high water solubility, low cytotoxicity, and stability in highly reductive environments. Notably, TTM-PhTTM NPs maintained a strong electron paramagnetic resonance (EPR) signal even after exposure to ascorbic acid for 24 h, and cell viability remained above 80 % even at concentrations up to 1400 μg/mL. Moreover, TTM-PhTTM NPs demonstrated a T1 longitudinal relaxation rate of 0.35 mM-1s-1, one of the highest recorded for metal-free organic magnetic nanomaterials. Following a 6-h incubation period, significant enhancements in imaging contrast were observed, with T1 signal intensities increasing as the concentration of TTM-PhTTM NPs increased. This study paves the way for the utilization of stable, biocompatible carbon-based radicals as MRI contrast agents, underscoring their potential for safe and effective biomedical imaging applications and providing a solid foundation for further development of carbon radical-based imaging technologies.

NIR-II Photoresponsive Magnetoliposomes for Remote-Controlled Release and Magnetic Resonance Imaging

Magnetic nanoparticles, especially iron oxide nanoparticles, have become versatile and widely used tools in nanomedicine due to their unique magnetic properties, biocompatibility, and tunable functionality. Liposomes have further enhanced the potential of iron oxide nanoparticles by serving as effective nanocarriers with advantages such as drug coencapsulation and enhanced molecular imaging properties. In this study, we present magnetoliposomes composed of ultrasmall free-floating iron oxide nanoparticles inside liposomes (LP-IONPs) and thermoresponsive phospholipids, which were designed as dual T2-T1 magnetic resonance imaging (MRI) contrast agents for image-guided liposome degradation and infrared light-responsive nanocarriers in the second biological window for remote-controlled drug delivery. We demonstrated a dynamic shift from T2 to T1 MRI contrast during intracellular degradation of LP-IONPs, along with successful light-activated drug release in cancer cells. Biodistribution studies using MRI and histological analysis confirmed their potential for in vivo applications. These results highlight the potential of LP-IONPs as image-guided and remote-controlled drug delivery systems.

Exploring the Application Potential of α-Synuclein Molecular Probes in Early Diagnosis of Parkinson's Disease: Focus on Imaging Methods

This review aims to explore the potential application of α-synuclein (α-syn) molecular probes in the early diagnosis of Parkinson's disease (PD), particularly through systematic evaluation using medical imaging methods. In recent years, The abnormal aggregation of α-syn within the central nervous system is now recognized as a central driver of PD pathophysiology, solidifying its role as a critical diagnostic and prognostic biomarker. Early diagnosis of PD is critical for enabling precision therapeutic interventions and mitigating neurodegenerative progression, thereby enhancing long-term functional outcomes and the quality of life. However, challenges remain in clinical practice, particularly concerning the late timing of diagnosis and the lack of specific biomarkers. By analyzing the existing literature, we will assess the effectiveness of different imaging techniques combined with α-syn probes and discuss their advantages and limitations in clinical applications. These imaging methods can provide visualization of early pathological changes, helping to improve the recognition rate of PD. Finally, we emphasize the importance of future research to explore new molecular probes and imaging technologies that can improve early diagnosis rates and treatment outcomes for PD.

Advancing cancer gene therapy: the emerging role of nanoparticle delivery systems

Gene therapy holds immense potential due to its ability to precisely target oncogenes, making it a promising strategy for cancer treatment. Advances in genetic science and bioinformatics have expanded the applications of gene delivery technologies beyond detection and diagnosis to potential therapeutic interventions. However, traditional gene therapy faces significant challenges, including limited therapeutic efficacy and the rapid degradation of genetic materials in vivo. To address these limitations, multifunctional nanoparticles have been engineered to encapsulate and protect genetic materials, enhancing their stability and therapeutic effectiveness. Nanoparticles are being extensively explored for their ability to deliver various genetic payloads-including plasmid DNA, messenger RNA, and small interfering RNA-directly to cancer cells. This review highlights key gene modulation strategies such as RNA interference, gene editing systems, and chimeric antigen receptor (CAR) technologies, alongside a diverse array of nanoscale delivery systems composed of polymers, lipids, and inorganic materials. These nanoparticle-based delivery platforms aim to improve targeted transport of genetic material into cancer cells, ultimately enhancing the efficacy of cancer therapies.

Recent advances in metal-organic frameworks for catalysing organic transformation

Metal-organic frameworks (MOFs) have garnered considerable attention due to their tunable properties, well-defined porosity, and structural versatility, making them effective catalysts for organic transformations. This review explores recent advances in MOF-based catalysis, emphasizing the roles of metal centres and organic linkers, as well as the synergistic effects arising from their combination. Additionally, guest molecule encapsulation and morphology modulation as effective strategies for improving catalytic efficiency are also discussed. Finally, future challenges and opportunities for MOFs as heterogenous catalysts are highlighted.

Photosystem I and ZIF-8 Interfacing: Entrapment and Immobilization

In this study, we explore the interfacing of Photosystem I (PSI) with the metal-organic framework (MOF) ZIF-8 (ZIF = zeolitic imidazolate framework) through encapsulation and surface immobilization methods, aimed at stabilizing PSI through biohybrid composite formation. PSI was successfully encapsulated within ZIF-8 (PSI@ZIF-8) and immobilized on ZIF-8 surfaces (PSI/ZIF-8) using a one-pot synthesis and surface impregnation technique, respectively. Characterization techniques including powder X-ray diffraction, Fourier transform infrared spectroscopy, and high-angle annular dark-field scanning transmission electron microscopy confirmed the formation and first-of-its-kind nanoscale visualization of the PSI/ZIF-8 composites. Spectroscopic analysis revealed that while PSI encapsulation resulted in minor structural changes potentially from scaffolding-induced stress and MOF building blocks, the overall protein integrity was maintained. Our study demonstrates that, in contrast to surface interfacing, ZIF-8 encapsulation provides a protective environment for PSI, enhancing its stability and retaining its functional properties, thereby offering an auspicious approach for the development of biohybrid materials in semi-artificial photosynthesis and other biotechnological applications.

In Situ Encapsulation of Organophosphorus Hydrolase into a Reduction-Catalytic Cu-MOF for Chemoenzymatic Cascade Conversion of Methyl Parathion to 4-Aminophenol

The highly toxic organophosphate pesticide methyl parathion (MP) is widely used in agriculture and thus poses significant risks to the environment. Organophosphorus hydrolase (OPH) can effectively catalyze the hydrolysis of MP, but generate a toxic product of 4-nitrophenol (4-NP), so the following reduction of 4-NP to 4-aminophenol (4-AP) with low toxicity but high economic value has been designed for sustainable development. Therefore, the cascade conversion of highly hazardous MP to economically valuable 4-AP is an attractive strategy for both environmental remediation and industrial applications. In this study, we synthesized a reduction-catalytic cupric metal-organic framework (CuBDC) in solvent-free aqueous phase and under mild conditions and in situ encapsulated OPH within CuBDC via a cation polyelectrolyte poly(allylamine hydrochloride) (PAH)-assisted biomineralization, creating a chemoenzymatic cascade catalyst (OPH-PAH@CuBDC) for MP conversion to 4-NP. The optimized OPH-PAH@CuBDC exhibited 154% hydrolysis activity of free OPH, 130% reduction activities of pure CuBDC, and an average cascade conversion rate of 6.2 μmol·min-1·gcatalyst-1, affording a complete conversion of 50 μM MP to 4-AP in only 6 min at low reductant concentration (20 mM NaBH4), due to the Cu2+-activation effect on the encapsulated OPH and the PAH-induced exposure of more reduction-catalytic sites, respectively. Furthermore, OPH-PAH@CuBDC retained 95% activity in 10 day storage and about 50% activity in 10 recycles. The results proved that OPH-PAH@CuBDC is a promising chemoenzymatic catalyst with great potential in practical applications for MP detoxification and resourcilization.

A Validated Ultrasound-Based Scoring System to Stratify Risk of Axillary Metastasis in Breast Cancer: AX-RADS (Axillary Imaging Reporting and Data System)

INTRODUCTION

Ultrasound is the imaging modality of choice for evaluation of axillary involvement in breast cancer, but is associated with variable sensitivity and specificity. Understanding the risk of axillary lymph node metastasis (ALNM) based on ultrasonographic and clinical features will inform treatment decisions. Our group aimed to create a scoring system to quantify the risk of ALNM based on ultrasound characteristics in breast cancer patients. We validated the model and tested it among different Memorial Sloan Kettering Breast Cancer Sentinel Lymph Node Metastasis Nomogram (MSK) subgroups.

METHODS

The ultrasound score was developed using data collected at a single institution from 2019 to 2021 by allocating points based on the regression coefficients of variables found to significantly predict ALNM. We validated the test statistics of our score at an outside institution. The index and validation cohorts were combined: 358 pooled patients were stratified by predicted ALNM positivity according to a validated nomogram based on primary tumor characteristics.

RESULTS

Between 2019 and 2021, in the validation cohort, the NPV for low risk (0-1) scores was 87%, while the PPV for high-risk (5 +) scores was 71%. Overall, in the combined cohort, 241 (67%) patients had low-risk (0-1) axillary ultrasound scores and 33 (9%) had high risk (5 +) scores. In this combined cohort, NPV was 84% (203/241 low-risk score patients were node negative), while PPV for high-risk scores was 85% (28/33 high-risk score patients were node positive). When stratified via the Memorial Sloan Kettering Breast Cancer Nomogram: Sentinel Lymph Node Metastasis predicted ALNM rates, the NPV of low-risk scores was 87%-89% for patients with < 50% predicted ALNM positivity. For patients with > 50% predicted ALNM positivity, the PPV of high-risk scores was 82%.

CONCLUSIONS

A scoring system to predict ALNM among biopsy-proven breast cancer patients undergoing upfront surgery was successfully developed from a multivariate model based on axillary ultrasound characteristics. Combining the axillary US scoring system with an additional validated nomogram based on primary tumor and patient characteristics may help foster better communication about ALNM risk to inform treatment decisions.