Kilogram-Scale Synthesis of Extremely Small Gadolinium Oxide Nanoparticles as a T1-Weighted Contrast Agent for Magnetic Resonance Imaging.

Magnetic resonance imaging contrast agents are frequently used in clinics to enhance the contrast between diseased and normal tissues. The previously reported poly(acrylic acid) stabilized exceedingly small gadolinium oxide nanoparticles (ES-GdON-PAA) overcame the problems of commercial Gd chelates, but limitations still exist, i.e., high r2/r1 ratio, long blood circulation half-life, and no data for large scale synthesis and formulation optimization. In this study, polymaleic acid (PMA) is found to be an ideal stabilizer to synthesize ES-GdONs. Compared with ES-GdON-PAA, the PMA-stabilized ES-GdON (ES-GdON-PMA) has a lower r2/r1 ratio (2.05, 7.0 T) and a lower blood circulation half-life (37.51 min). The optimized ES-GdON-PMA-9 has an exceedingly small particle size (2.1 nm), excellent water dispersibility, and stability. A facile, efficient, and environmental friendly synthetic method is developed for large-scale synthesis of the ES-GdONs-PMA. The weight of the optimized freeze-dried ES-GdON-PMA-26 synthesized in a 20 L of reactor reaches the kilogram level. The formulation optimization is also finished, and the concentrated ES-GdON-PMA-26 formulation (CGd = 100 mm) after high-pressure steam sterilization possesses eligible physicochemical properties (i.e., pH value, osmolality, viscosity, and density) for investigational new drug application.

MRI-Guided Tumor Therapy Based on Synergy of Ferroptosis, Immunosuppression Reversal and Disulfidptosis.

Triple negative breast cancer (TNBC) cells have a high demand for oxygen and glucose to fuel their growth and spread, shaping the tumor microenvironment (TME) that can lead to a weakened immune system by hypoxia and increased risk of metastasis. To disrupt this vicious circle and improve cancer therapeutic efficacy, a strategy is proposed with the synergy of ferroptosis, immunosuppression reversal and disulfidptosis. An intelligent nanomedicine GOx-IA@HMON@IO is successfully developed to realize this strategy. The Fe release behaviors indicate the glutathione (GSH)-responsive degradation of HMON. The results of titanium sulfate assay, electron spin resonance (ESR) spectra, 5,5'-Dithiobis-(2-nitrobenzoic acid (DTNB) assay and T1-weighted magnetic resonance imaging (MRI) demonstrate the mechanism of the intelligent iron atom (IA)-based cascade reactions for GOx-IA@HMON@IO, generating robust reactive oxygen species (ROS). The results on cells and mice reinforce the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis triggered by the GOx-IA@HMON@IO with the following steps: 1) GSH peroxidase 4 (GPX4) depletion by disulfidptosis; 2) IA-based cascade reactions; 3) tumor hypoxia reversal; 4) immunosuppression reversal; 5) GPX4 depletion by immunotherapy. Based on the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis, the intelligent nanomedicine GOx-IA@HMON@IO can be used for MRI-guided tumor therapy with excellent biocompatibility and safety.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

OBJECTIVE: To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). METHODS: This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. RESULTS: A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). CONCLUSIONS: EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Radiotherapy-sensitized cancer immunotherapy via cGAS-STING immune pathway by activatable nanocascade reaction.

Radiotherapy-induced immune activation holds great promise for optimizing cancer treatment efficacy. Here, we describe a clinically used radiosensitizer hafnium oxide (HfO2) that was core coated with a MnO2 shell followed by a glucose oxidase (GOx) doping nanoplatform (HfO2@MnO2@GOx, HMG) to trigger ferroptosis adjuvant effects by glutathione depletion and reactive oxygen species production. This ferroptosis cascade potentiation further sensitized radiotherapy by enhancing DNA damage in 4T1 breast cancer tumor cells. The combination of HMG nanoparticles and radiotherapy effectively activated the damaged DNA and Mn2+-mediated cGAS-STING immune pathway in vitro and in vivo. This process had significant inhibitory effects on cancer progression and initiating an anticancer systemic immune response to prevent distant tumor recurrence and achieve long-lasting tumor suppression of both primary and distant tumors. Furthermore, the as-prepared HMG nanoparticles "turned on" spectral computed tomography (CT)/magnetic resonance dual-modality imaging signals, and demonstrated favorable contrast enhancement capabilities activated by under the GSH tumor microenvironment. This result highlighted the potential of nanoparticles as a theranostic nanoplatform for achieving molecular imaging guided tumor radiotherapy sensitization induced by synergistic immunotherapy.

Kilogram scale facile synthesis and systematic characterization of a Gd-macrochelate as T1-weighted magnetic resonance imaging contrast agent.

To overcome the problems of commercial magnetic resonance imaging (MRI) contrast agents (CAs) (i.e., small molecule Gd chelates), we have proposed a new concept of Gd macrochelates based on the coordination of Gd3+ and macromolecules, e.g., poly(acrylic acid) (PAA). To further decrease the r2/r1 ratio of the reported Gd macrochelates that is an important factor for T1 imaging, in this study, a superior macromolecule hydrolyzed polymaleic anhydride (HPMA) was found to coordinate Gd3+. The synthesis conditions were optimized and the generated Gd-HPMA macrochelate was systematically characterized. The obtained Gd-HPMA29 synthesized in a 100 L of reactor has a r1 value of 16.35 mM-1 s-1 and r2/r1 ratio of 2.05 at 7.0 T, a high Gd yield of 92.7% and a high product weight (1074 g), which demonstrates the feasibility of kilogram scale facile synthesis. After optimization of excipients and sterilization at a high temperature, the obtained Gd-HPMA30 formulation has a pH value of 7.97, osmolality of 691 mOsmol/kg water, density of 1.145 g/mL, and viscosity of 2.2 cP at 20 â„ƒ or 1.8 cP at 37 â„ƒ, which meet all specifications and physicochemical criteria for clinical injections indicating the immense potential for clinical applications.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

OBJECTIVES: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB. METHODS: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning-based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A "TB score" was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model. RESULTS: CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08-91.05%. A moderate to strong correlation was demonstrated between the AI model-quantified TB score and the radiologist-estimated CT score. CONCLUSIONS: The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB. KEY POINTS: • Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis. • Artificial intelligence helps clinicians to assess patients with tuberculosis. • Pulmonary tuberculosis disease activity and treatment management can be improved.

Structural and functional radiomics for lung cancer.

INTRODUCTION: Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS: Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION: The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality.

OBJECTIVES: To evaluate the image quality of ultralow-dose computed tomography (ULDCT) reconstructed with knowledge-based iterative model reconstruction (IMR) in patients with pulmonary tuberculosis (TB). METHODS: This IRB-approved prospective study enrolled 59 consecutive patients (mean age, 43.9 ± 16.6 years; F:M 18:41) with known or suspected pulmonary TB. Patients underwent a low-dose CT (LDCT) using automatic tube current modulation followed by an ULDCT using fixed tube current. Raw image data were reconstructed with filtered-back projection (FBP), hybrid iterative reconstruction (iDose), and IMR. Objective measurements including CT attenuation, image noise, and contrast-to-noise ratio (CNR) were assessed and compared using repeated-measures analysis of variance. Overall image quality and visualization of normal and pathological findings were subjectively scored on a five-point scale. Radiation output and subjective scores were compared by the paired Student t test and Wilcoxon signed-rank test, respectively. RESULTS: Compared with FBP and iDose, IMR yielded significantly lower noise and higher CNR values at both dose levels (p < 0.01). Subjective ratings for pathological findings including centrilobular nodules, consolidation, tree-in-bud, and cavity were significantly better with ULDCT IMR images than those with LDCT iDose images (p < 0.01), but blurred edges were observed. With IMR implementation, a 59% reduction of the mean effective dose was achieved with ULDCT (0.28 ± 0.02 mSv) compared with LDCT (0.69 ± 0.15 mSv) without impairing image quality (p < 0.001). CONCLUSIONS: IMR offers considerable noise reduction and improvement in image quality for patients with pulmonary TB undergoing chest ULDCT at an effective dose of 0.28 mSv. KEY POINTS: • Radiation dose is a major concern for tuberculosis patients requiring repeated follow-up CT. • IMR allows substantial radiation dose reduction in chest CT without compromising image quality. • ULDCT reconstructed with IMR allows accurate depiction of CT features of pulmonary tuberculosis.

Radiation Dose Reduction by Using CT with Iterative Model Reconstruction in Patients with Pulmonary Invasive Fungal Infection.

Purpose To compare the diagnostic quality of reduced radiation dose computed tomography (CT) with iterative model reconstruction (IMR) versus that of conventional low-dose CT in patients with pulmonary invasive fungal infection. Materials and Methods This prospective observational study included 48 patients (mean age ± standard deviation, 39.9 years ± 11.3) known to have or suspected of having pulmonary invasive fungal infection between October 2016 and July 2017. Patients underwent CT with IMR (at 80 kV with 20 mA) immediately after low-dose CT (at 80 kV with automatic exposure control). Images were reconstructed by using a hybrid iterative reconstruction (HIR) algorithm and IMR. Two radiologists independently assessed subjective image quality, noise, and visibility of normal and abnormal findings by using a five-point scale. Objective measurements, including image noise, contrast-to-noise ratio (CNR), and corresponding figure of merit (FOM), were compared by using repeated-measures analysis of variance with Bonferroni post hoc tests for multiple comparisons. Results The mean effective dose was 0.3 mSv ± 0.3 for CT with IMR and 0.7 mSv ± 0.2 for low-dose CT (P < .01). When the image noise and CNR were normalized to the effective dose, CT images obtained with IMR had significantly higher FOM than did other image series (P < .0001). Subjectively, visibility of CT features of invasive fungal infection on CT scans reconstructed with IMR was rated as noninferior to that on low-dose CT scans reconstructed with HIR, except for the halo sign. Conclusion CT with IMR had approximately 60% dose reduction compared with conventional low-dose CT, with reduced noise and improved depiction of abnormal findings, in patients with pulmonary invasive fungal infection.

Use of intravoxel incoherent motion diffusion-weighted MR imaging for assessment of treatment response to invasive fungal infection in the lung.

OBJECTIVES: The purpose of this study was to determine whether intravoxel incoherent motion (IVIM) -derived parameters and apparent diffusion coefficient (ADC) could act as imaging biomarkers for predicting antifungal treatment response. METHODS: Forty-six consecutive patients (mean age, 33.9 ± 13.0 y) with newly diagnosed invasive fungal infection (IFI) in the lung according to EORTC/MSG criteria were prospectively enrolled. All patients underwent diffusion-weighted magnetic resonance (MR) imaging at 3.0 T using 11 b values (0-1000 sec/mm2). ADC, pseudodiffusion coffiecient D*, perfusion fraction f, and the diffusion coefficient D were compared between patients with favourable (n=32) and unfavourable response (n=14). RESULTS: f values were significantly lower in the unfavourable response group (12.6%±4.4%) than in the favourable response group (30.2%±8.6%) (Z=4.989, P<0.001). However, the ADC, D, and D* were not significantly different between the two groups (P>0.05). Receiver operating characteristic curve analyses showed f to be a significant predictor for differentiation, with a sensitivity of 93.8% and a specificity of 92.9%. CONCLUSIONS: IVIM-MRI is potentially useful in the prediction of antifungal treatment response to patients with IFI in the lung. Our results indicate that a low perfusion fraction f may be a noninvasive imaging biomarker for unfavourable response. KEY POINTS: • Recognition of IFI indicating clinical outcome is important for treatment decision-making. • IVIM can reflect diffusion and perfusion information of IFI lesions separately. • Perfusion characteristics of IFI lesions could help differentiate treatment response. • An initial low f may predict unfavourable response in IFI.

Prognostic Value of Diffusion-Weighted Imaging (DWI) Apparent Diffusion Coefficient (ADC) in Patients with Hyperacute Cerebral Infarction Receiving rt-PA Intravenous Thrombolytic Therapy.

BACKGROUND The aim of this study was to investigate the potential value of apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) in the prognosis of patients with hyperacute cerebral infarction (HCI) receiving intravenous thrombolytic therapy with recombinant tissue plasminogen activator (rt-PA). MATERIAL AND METHODS From June 2012 to June 2015, 58 cases of HCI (<6 h) undergoing rt-PA intravenous thrombolytic therapy (thrombolysis group) and 70 cases of HCI (<6 h) undergoing conventional antiplatelet and anticoagulant therapy (control group) in the same period were collected. DWI was conducted on all the subjects, and ADC maps were generated with Functool software to quantify ADC value. The clinical outcomes of HCI patients were observed for 3 months, and prognostic factors were analyzed. RESULTS Before thrombolysis treatment, the lesion area presented high signal intensity on DWI map and low signal intensity on ADC map, and gradually weakened signal intensity on DWI map and gradually enhanced signal intensity on ADC map were observed after thrombolysis. The ADC values of the thrombolysis group were significantly higher than those of the control group after treatment (24 h, 7 d, 30 d, and 90 d) (all P<0.05), and the ADC and rADC values in the thrombolysis group gradually increased over time (all P<0.05). Multiple logistic regression analysis showed that baseline National Institutes of Health Stroke Scale (NIHSS) score, baseline rADC value, and stroke history were the independent factors for the prognosis of HIC patients with thrombolysis (all P<0.05). CONCLUSIONS The values of ADC and rADC may provide guidance in the prognosis of HCI patients receiving rt-PA, and the baseline rADC value is the protective factor for the prognosis of HCI patients receiving rt-PA.

Lung MRI of invasive fungal infection at 3 Tesla: evaluation of five different pulse sequences and comparison with multidetector computed tomography (MDCT).

OBJECTIVES: To evaluate the diagnostic performance of five MR sequences to detect pulmonary infectious lesions in patients with invasive fungal infection (IFI), using multidetector computed tomography (MDCT) as the reference standard. METHODS: Thirty-four immunocompromised patients with suspected IFI underwent MDCT and MRI. The MR studies were performed using five pulse sequences at 3.0 T: T2-weighted turbo spin echo (TSE), short-tau inversion recovery (STIR), spectrally selective attenuated inversion recovery (SPAIR), T1-weighted high resolution isotropic volume excitation (e-THRIVE) and T1-weighted fast field echo (T1-FFE). The size, lesion-to-lung contrast ratio and the detectability of pulmonary lesions on MR images were assessed. Image quality and artefacts on different sequences were also rated. RESULTS: A total of 84 lesions including nodules (n = 44) and consolidation (n = 40) were present in 75 lobes. SPAIR and e-THRIVE images achieved high overall lesion-related sensitivities for the detection of pulmonary abnormalities (90.5% and 86.9%, respectively). STIR showed the highest lesion-to-lung contrast ratio for nodules (21.8) and consolidation (17.0), whereas TSE had the fewest physiological artefacts. CONCLUSIONS: MRI at 3.0 T can depict clinically significant pulmonary IFI abnormalities with high accuracy compared to MDCT. SPAIR and e-THRIVE are preferred sequences for the detection of infectious lesions of 5 mm and larger. KEY POINTS: • A radiation-free radiological method is desirable for assessing pulmonary infectious lesions • MRI at 3 T can depict lung infiltrates with good concordance to MDCT • SPAIR and e-THRIVE are favourable sequences for the detection of pulmonary lesions • The greatest benefit is for the diagnosis of lesions larger than 5 mm.

Cancer Radiosensitization Nanoagent to Activate cGAS-STING Pathway for Molecular Imaging Guided Synergistic Radio/Chemo/Immunotherapy.

Immunotherapy has emerged as an innovative strategy with the potential to improve outcomes in cancer patients. Recent evidence indicates that radiation-induced DNA damage can activate the cyclic-GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway to enhance the antitumor immune response. Even so, only a small fraction of patients currently benefits from radioimmunotherapy due to the radioresistance and the inadequate activation of the cGAS-STING pathway. Herein, this work integrates hafnium oxide (HfO2) nanoparticles (radiosensitizer) and 7-Ethyl-10-hydroxycamptothecin (SN38, chemotherapy drug, STING agonist) into a polydopamine (PDA)-coated core-shell nanoplatform (HfO2@PDA/Fe/SN38) to achieve synergistic chemoradiotherapy and immunotherapy. The co-delivery of HfO2/SN38 greatly enhances radiotherapy efficacy by effectively activating the cGAS-STING pathway, which then triggers dendritic cells maturation and CD8+ T cells recruitment. Consequently, the growth of both primary and abscopal tumors in tumor-bearing mice is efficiently inhibited. Moreover, the HfO2@PDA/Fe/SN38 complexes exhibit favorable magnetic resonance imaging (MRI)/photoacoustic (PA) bimodal molecular imaging properties. In summary, these developed multifunctional complexes have the potential to intensify immune activation to realize simultaneous cancer Radio/Chemo/Immunotherapy for clinical translation.

Hollow mesoporous calcium peroxide nanoparticles for drug-free tumor calcicoptosis therapy.

Calcium ions (Ca2+) participate in the regulation of cellular apoptosis as a second messenger. Calcium overload, which refers to the abnormal elevation of intracellular Ca2+ concentration, is a factor that can lead to cell death. Here, based on the unique biological effects of Ca2+, hollow mesoporous calcium peroxide nanoparticles (HMCPN) were developed by a facile hydrolysis-precipitation method for drug-free tumor calcicoptosis therapy. The average pore size of the optimized HMCPN17 is 6.4 nm, and the surface area is 81.3 m2/g, which enables HMCPN17 with high drug loading capability. The Ca2+ release from HMCPN17 is much faster at pH 6.8 than that at pH 7.4, which can be ascribed to the acid-triggered conversion of HMCPN17 to Ca2+ and H2O2, indicating a pH-responsive decomposition behavior of HMCPN17. The high drug loading contents of doxorubicin (DOX) and/or sorafenib (SFN) indicate that HMCPN17 can be employed as a generic drug delivery system (DDS). The in vitro and in vivo results reinforce the high calcicoptosis therapeutic efficacy of tumors by our HMCPN17 without drug loading, which can be attributed to the efficient accumulation in tumors and the ability of H2O2 and Ca2+ production at acidic TME. Our HMCPN17 has broad application prospect for construction of multi-drug-loaded composite nanomaterials with diversified functions for the treatment of tumors. STATEMENT OF SIGNIFICANCE: The combination of hollow mesoporous nanomaterials and calcium peroxide nanoparticles has a wide range of applications in the synergistic treatment of tumors. In this study, hollow mesoporous calcium peroxide nanoparticles (HMCPN) were developed based on a simple hydrolysis-precipitation method for tumor calcicoptosis therapy without drug loading. The high drug loading contents of DOX and/or SFN indicate that our HMCPN can serve as a generic DDS. The experimental results demonstrated the high calcicoptosis therapeutic efficacy of HMCPN on tumors even without drug loading.

Double-Layered Hollow Mesoporous Cuprous Oxide Nanoparticles for Double Drug Sequential Therapy of Tumors.

Cancer stem cells (CSCs) are one of the determinants of tumor heterogeneity and are characterized by self-renewal, high tumorigenicity, invasiveness, and resistance to various therapies. To overcome the resistance of traditional tumor therapies resulting from CSCs, a strategy of double drug sequential therapy (DDST) for CSC-enriched tumors is proposed in this study and is realized utilizing the developed double-layered hollow mesoporous cuprous oxide nanoparticles (DL-HMCONs). The high drug-loading contents of camptothecin (CPT) and all-trans retinoic acid (ATRA) demonstrate that the DL-HMCON can be used as a generic drug delivery system. ATRA and CPT can be sequentially loaded in and released from CPT3@ATRA3@DL-HMCON@HA. The DDST mechanisms of CPT3@ATRA3@DL-HMCON@HA for CSC-containing tumors are demonstrated as follows: 1) the first release of ATRA from the outer layer induces differentiation from CSCs with high drug resistance to non-CSCs with low drug resistance; 2) the second release of CPT from the inner layer causes apoptosis of non-CSCs; and 3) the third release of Cu+ from DL-HMCON itself triggers the Fenton-like reaction and glutathione depletion, resulting in ferroptosis of non-CSCs. This CPT3@ATRA3@DL-HMCON@HA is verified to possess high DDST efficacy for CSC-enriched tumors with high biosafety.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs). MATERIALS AND METHODS: Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test. RESULTS: Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05). CONCLUSION: A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

A strategy of "adding fuel to the flames" enables a self-accelerating cycle of ferroptosis-cuproptosis for potent antitumor therapy.

Cuproptosis in antitumor therapy faces challenges from copper homeostasis efflux mechanisms and high glutathione (GSH) levels in tumor cells, hindering copper accumulation and treatment efficacy. Herein, we propose a strategy of "adding fuel to the flames" for potent antitumor therapy through a self-accelerating cycle of ferroptosis-cuproptosis. Disulfiram (DSF) loaded hollow mesoporous copper-iron sulfide (HMCIS) nanoparticle with conjugation of polyethylene glycol (PEG) and folic acid (FA) (i.e., DSF@HMCIS-PEG-FA) was developed to swiftly release DSF, H2S, Cu2+, and Fe2+ in the acidic tumor microenvironment (TME). The hydrogen peroxide (H2O2) levels and acidity within tumor cells enhanced by the released H2S induce acceleration of Fenton (Fe2+) and Fenton-like (Cu2+) reactions, enabling the powerful tumor ferroptosis efficacy. The released DSF acts as a role of "fuel", intensifying catalytic effect ("flame") in tumor cells through the sustainable Fenton chemistry (i.e., "add fuel to the flames"). Robust ferroptosis in tumor cells is characterized by serious mitochondrial damage and GSH depletion, leading to excess intracellular copper that triggers cuproptosis. Cuproptosis disrupts mitochondria, compromises iron-sulfur (Fe-S) proteins, and elevates intracellular oxidative stress by releasing free Fe3+. These interconnected processes form a self-accelerating cycle of ferroptosis-cuproptosis with potent antitumor capabilities, as validated in both cancer cells and tumor-bearing mice.

Clinical prediction of magnetic resonance image compilation in patients with mild cognitive impairment.

This study aims to investigate the clinical prediction of magnetic resonance image compilation (MAGiC) and magnetic resonance image (MRI) in early diagnosis of the patients with mild cognitive impairment. This study is a retrospective randomized controlled clinical trial, and all patients are divided into following two groups: experiment group and control group. Patients in the experiment group are detected by MAGiC, and patients in the control group are detected by MRI; the clinical material from the two groups of patients with MCI are collected, and then Wechsler Memory Scale-Logical Memory (WMS-LM) and Mini-Mental State Examination (MMSE) are recorded by follow-up. Images by MAGiC have higher accuracy and definition compared with those by MRI. WMS-LM score and MMSE score in the experiment group are significantly better than those in the control group. We can conclude that MAGiC is a promising way to evaluate the clinical prediction in patients with MCI.

Combining ADC values in DWI with rCBF values in arterial spin labeling (ASL) for the diagnosis of mild cognitive impairment (MCI).

We aimed to investigate the role of combined apparent diffusion coefficient (ADC) values and relative cerebral blood flow (rCBF) values in the diagnosis of mild cognitive impairment (MCI) patients. The present prospective research enrolled 156 MCI patients and 58 healthy elderly people who came to our hospital from January 2021 to February 2023. T1W, T2W, diffusion-weighted imaging, and arterial spin labeling sequences were performed on all subjects, and ADC values and rCBF values were measured at the workstation. Clinical and demographic data of all patients were collected while mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores were used to assess patients' cognitive abilities. The MCI group had significantly lower rCBF values in the left frontal lobe, left occipital lobe, right frontal lobe, and right occipital lobe than the HC group. The ADC values in the left frontal lobe as well as the right frontal lobe were remarkably elevated in the MCI group than in the HC group. MoCA and MMSE scores were positively correlated with rCBF values in the left frontal, right frontal, left occipital, and right occipital lobes and negatively correlated with ADC values in the left and right frontal lobes. Combined ADC values and rCBF values from the left frontal lobe for the diagnosis of MCI had a higher sensitivity and specificity with the AUC was 0.877, sensitivity 81.0%, specificity 82.7%. Additionally, pressure fasting plasma glucose, ADC of the left frontal lobe, right frontal lobe, rCBF of left frontal lobe and rCBF of left frontal lobe were the risk factors of patients with MCI. In summary, our results indicated that the ADC values and rCBF values were changed in MCI group compared to HC group and correlated with MMSE and MoCA scores.