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Exploring Land use and climate change-based multi-scenario simulation of ecosystem service trade-offs/synergies is of great importance to regional ecological security and sustainable development. Taking the Central Yunnan Urban Agglomeration (CYUA) as a case study, six different scenarios of LULC-RCP were established to quantitatively assess four key ecosystem services(ESs) of water yield (WY), carbon stock (CS), soil conservation (SR) and habitat quality (HQ) with multiple objective programming and patch-generating land use simulation(MOP-PLUS) and integrated valuation of ecosystem services and tradeoffs (InVEST) models. The ESs were revealed regarding spatio-temporal trade-offs/synergies using Spearman correlation and geographically weighted regression (GWR). It was found that: (1)the ESs in CYUA is characterized with high spatial heterogeneity in 2030; specifically, the distribution of WY and SR was low in the northwestern region and high in the southeastern region, while the distribution of HQ and CS was high in the western region and the periphery, and low in the eastern and central regions; (2) the trade-offs between WY-HQ, and WY-CS, and the synergies between WY-SR, HQ-SR, HQ-CS, HQ-CS, and HQ-SR; (3) under the six different scenarios, the spatial distribution of trade-offs/synergies between the four ESs was consistent: the SR-HQ, SR-CS, and WY-CS showed an overall weak synergistic relationship; the HQ-CS showed an overall weak trade-offs; the HQ-WY, CS-WY showed an overall weak synergistic relationship in the northern and southern areas and an overall weak trade-off relationship in the center. The findings of this study may provide a theoretical foundation for ecosystem management in CYUA and offer technical support for the evaluation of national land space.

Objectives To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC). Methods We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3–4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3–4 cycles chemotherapy) for evaluating models’ predictive ability before and after the complete course, and (c) testing cohort II ( n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection. Results PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000). Conclusions CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients. Key Points • Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.

Myocardial injury triggers intense oxidative stress, inflammatory response, and cytokine release, which are essential for myocardial repair and remodeling. Excess reactive oxygen species (ROS) scavenging and inflammation elimination have long been considered to reverse myocardial injuries. However, the efficacy of traditional treatments (antioxidant, anti-inflammatory drugs and natural enzymes) is still poor due to their intrinsic defects such as unfavorable pharmacokinetics and bioavailability, low biological stability, and potential side effects. Nanozyme represents a candidate to effectively modulate redox homeostasis for the treatment of ROS related inflammation diseases. We develop an integrated bimetallic nanozyme derived from metal-organic framework (MOF) to eliminate ROS and alleviate inflammation. The bimetallic nanozyme (Cu-TCPP-Mn) is synthesized by embedding manganese and copper into the porphyrin followed by sonication, which could mimic the cascade activities of superoxide dismutase (SOD) and catalase (CAT) to transform oxygen radicals to hydrogen peroxide, followed by the catalysis of hydrogen peroxide into oxygen and water. Enzyme kinetic analysis and oxygen-production velocities analysis were performed to evaluate the enzymatic activities of Cu-TCPP-Mn. We also established myocardial infarction (MI) and myocardial ischemia-reperfusion (I/R) injury animal models to verify the ROS scavenging and anti-inflammation effect of Cu-TCPP-Mn. As demonstrated by kinetic analysis and oxygen-production velocities analysis, Cu-TCPP-Mn nanozyme possesses good performance in both SOD- and CAT-like activities to achieve synergistic ROS scavenging effect and provide protection for myocardial injury. In both MI and I/R injury animal models, this bimetallic nanozyme represents a promising and reliable technology to protect the heart tissue from oxidative stress and inflammation-induced injury, and enables the myocardial function to recover from otherwise severe damage. This research provides a facile and applicable method to develop a bimetallic MOF nanozyme, which represents a promising alternative to the treatment of myocardial injuries.

Objectives To evaluate the utility of arterial spin labeling (ASL) for the identification of kidney allografts with underlying pathologies, particularly those with stable graft function. Methods A total of 75 patients, including 18 stable grafts with normal histology (normal group), 21 stable grafts with biopsy-proven pathology (subclinical pathology group), and 36 with unstable graft function (unstable graft group), were prospectively examined by ASL magnetic resonance imaging. Receiver operating characteristic curves were generated to calculate the area under the curve (AUC), sensitivity, and specificity. Results Patient demographics among the 3 groups were comparable. Compared with the normal group, kidney allograft cortical ASL values decreased in the subclinical pathology group and the unstable graft group (204.7 ± 44.9 ml/min/100 g vs 152.5 ± 38.9 ml/min/100 g vs 92.3 ± 37.4 ml/min/100 g, p < 0.001). The AUC, sensitivity, and specificity for discriminating allografts with pathologic changes from normal allografts were 0.92 (95% CI, 0.83–0.97), 71.9%, and 100% respectively by cortical ASL and 0.82 (95% CI, 0.72–0.90), 54.4%, and 100% respectively by serum creatinine. The cortical ASL identified allografts with subclinical pathology among patients with stable graft function with an AUC of 0.80 (95% CI, 0.64–0.91), sensitivity of 57.1%, and specificity of 88.9%. Combined use of proteinuria and cortical ASL could improve the sensitivity and specificity to 76.2% and 100% respectively for distinguishing the subclinical pathology group from the normal group. Conclusions Cortical ASL is useful for the identification of allografts with underlying pathologies. More importantly, ASL showed promise as a non-invasive tool for the clinical translation of identifying kidney allografts with subclinical pathology. Key Points • Cortical ASL values were decreased in kidney allografts with subclinical pathologic changes as compared with normal allografts (152.5 ± 38.9 ml/min/100 g vs 204.7 ± 44.9 ml/min/100 g, p < 0.001). • Cortical ASL differentiated allografts with pathologic changes and subclinical pathology group from normal group with an AUC of 0.92 (95% CI, 0.83–0.97) and 0.80 (95% CI, 0.64–0.91) respectively. • Cortical ASL discriminated allografts with underlying pathologic changes from normal allografts with a specificity of 100%, and combined use of proteinuria and cortical ASL values could also achieve 100% specificity for discriminating allografts with subclinical pathology from normal allografts.

Background Tumor phototherapy especially photodynamic therapy (PDT) or photothermal therapy (PTT), has been considered as an attractive strategy to elicit significant immunogenic cell death (ICD) at an optimal tumor retention of PDT/PTT agents. Heptamethine cyanine dye (IR-780), a promising PDT/PTT agent, which can be used for near-infrared (NIR) fluorescence/photoacoustic (PA) imaging guided tumor phototherapy, however, the strong hydrophobicity, short circulation time, and potential toxicity in vivo hinder its biomedical applications. To address this challenge, we developed mesoporous polydopamine nanoparticles (MPDA) with excellent biocompatibility, PTT efficacy, and PA imaging ability, facilitating an efficient loading and protection of hydrophobic IR-780. Results The IR-780 loaded MPDA (IR-780@MPDA) exhibited high loading capacity of IR-780 (49.7 wt%), good physiological solubility and stability, and reduced toxicity. In vivo NIR fluorescence and PA imaging revealed high tumor accumulation of IR-780@MPDA. Furthermore, the combined PDT/PTT of IR-780@MPDA could induce ICD, triggered immunotherapeutic response to breast tumor by the activation of cytotoxic T cells, resulting in significant suppression of tumor growth in vivo. Conclusion This study demonstrated that the as-developed compact and biocompatible platform could induce combined PDT/PTT and accelerate immune activation via excellent tumor accumulation ability, offering multimodal tumor theranostics with negligible systemic toxicity. Graphical Abstract

Objective To compare the DWI-Alberta Stroke Program Early Computed Tomography Score calculated by a deep learning–based automatic software tool (eDWI-ASPECTS) with the neuroradiologists’ evaluation for the acute stroke, with emphasis on its performance on 10 individual ASPECTS regions, and to determine the reasons for inconsistencies between eDWI-ASPECTS and neuroradiologists’ evaluation. Methods This retrospective study included patients with middle cerebral artery stroke who underwent MRI from 2010 to 2019. All scans were evaluated by eDWI-ASPECTS and two independent neuroradiologists (with 15 and 5 years of experience in stroke study). Inter-rater agreement and agreement between manual vs. automated methods for total and each region were evaluated by calculating Kendall’s tau-b, intraclass correlation coefficient (ICC), and kappa coefficient. Results In total, 309 patients met our study criteria. For total ASPECTS, eDWI-ASPECTS and manual raters had a strong positive correlation (Kendall’s tau-b = 0.827 for junior raters vs. eDWI-ASPECTS; Kendall’s tau-b = 0.870 for inter-raters; Kendall’s tau-b = 0.848 for senior raters vs. eDWI-ASPECTS) and excellent agreement (ICC = 0.923 for junior raters and automated scores; ICC = 0.954 for inter-raters; ICC = 0.939 for senior raters and automated scores). Agreement was different for individual ASPECTS regions. All regions except for M5 region (κ = 0.216 for junior raters and automated scores), internal capsule (κ = 0.525 for junior raters and automated scores), and caudate (κ = 0.586 for senior raters and automated scores) showed good to excellent concordance. Conclusion The eDWI-ASPECTS performed equally well as senior neuroradiologists’ evaluation, although interference by uncertain scoring rules and midline shift resulted in poor to moderate consistency in the M5, internal capsule, and caudate nucleus regions. Key Points • The eDWI-ASPECTS based on deep learning perform equally well as senior neuroradiologists’ evaluations. • Among the individual ASPECTS regions, the M5, internal capsule, and caudate regions mainly affected the overall consistency. • Uncertain scoring rules and midline shift are the main reasons for regional inconsistency.

ObjectiveTo evaluate the added benefit of diffusion-weighted imaging (DWI) over clinical parameters in predicting kidney allograft function decline.MethodsData from 97 patients with DWI of the kidney allograft were retrospectively analyzed. The DWI signals were analyzed with both the mono-exponential and bi-exponential models, yielding total apparent diffusion coefficient (ADCT), true diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp). Three predictive models were constructed: Model 1 with clinical parameters, Model 2 with DWI parameters, and Model 3 with both clinical and DWI parameters. The predictive capability of each model was compared by calculating the area under the receiver-operating characteristic curve (AUROC).ResultsForty-five patients experienced kidney allograft function decline during a median follow-up of 98 months. The AUROC for Model 1 gradually decreased with follow-up time > 40 months, whereas Model 2 and Model 3 maintained relatively stable AUROCs. The AUROCs of Model 1 and Model 2 were not statistically significant. Multivariable analysis showed that the Model 3 included cortical D (HR = 3.93, p = 0.001) and cortical fp (HR = 2.85, p = 0.006), in addition to baseline estimated glomerular filtration rate (eGFR) and proteinuria. The AUROCs for Model 3 were significantly higher than those for Model 1 at 60-month (0.91 vs 0.86, p = 0.02) and 84-month (0.90 vs 0.83, p = 0.007) follow-up.ConclusionsDWI parameters were comparable to clinical parameters in predicting kidney allograft function decline. Integrating cortical D and fp into the clinical model with baseline eGFR and proteinuria may add prognostic value for long-term allograft function decline.Critical relevance statementOur findings suggested that cortical D and fp derived from IVIM-DWI increased the performance to predict long-term kidney allograft function decline. This preliminary study provided basis for the utility of multi-b DWI for managing patients with a kidney transplant.Key points• Both clinical and multi-b DWI parameters could predict kidney allograft function decline.• The ability to predict kidney allograft function decline was similar between DWI and clinical parameters.• Cortical D and fp derived from IVIM-DWI increased the performance to predict long-term kidney allograft function decline.

Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.

Purpose To develop and validate a dual-energy CT (DECT)–based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC). Methods A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed. Results The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree. Conclusion The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.

Background To investigate the merit of T1rho relaxation for the evaluation of liver fibrosis, inflammatory activity, and liver injury monitoring in a carbon tetrachloride (CCl 4 )-induced rat model. Methods Model rats from CCl 4 -induced liver fibrosis (fibrosis group: n  = 41; regression group: n  = 20) and control ( n  = 11) groups underwent black blood T1rho magnetic resonance (MR) imaging (MRI). Injection of CCl 4 was done twice weekly for up to 12 weeks in the fibrosis group and for up to 6 weeks in the regression group. MR scanning time points were at baseline and at 2, 4, 6, 8, 10 and 12 weeks after CCl 4 injection in the fibrosis group and at baseline and at 2, 4, 6 (CCl 4 withdrawal), 7, 8, 10 and 12 weeks in the regression group. Results In the fibrosis group, liver T1rho values increased gradually within week 8 and then decreased. In the regression group, T1rho values dropped gradually after the withdrawal of CCl 4 and fell below those at baseline. The T1rho values at S0 were lower than those at any other stage (all P  < 0.05). The T1rho values at G0 were significantly lower than those at any other grade, and G1 was lower than G2 (all P  < 0.01). The T1rho values mildly correlated with fibrosis stages ( r  = 0.362) and moderately correlated with grades of inflammation ( r  = 0.568). The T1rho values of rats with the same inflammation grades showed no significant difference among different fibrosis stages, and the T1rho values at S3 showed a significant difference among different grades of inflammation ( P  = 0.024). Inflammation grade was an independent variable associated with T1rho values ( P  < 0.001). Conclusion T1rho MRI can be used to monitor CCl 4 -induced liver injury, and inflammatory activity had a greater impact on liver T1rho values than fibrosis.