Identification of neurotransmitter imbalances in the cingulate cortex of NMOSD patients using magnetic resonance spectroscopy.

Cognitive impairment affects 29-67% of patients with neuromyelitis optica spectrum disorder. Previous studies have reported glutamate homeostasis disruptions in astrocytes, leading to imbalances in gamma-aminobutyric acid levels. However, the association between these neurotransmitter changes and cognitive deficits remains inadequately elucidated. Point RESolved Spectroscopy and Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy techniques were utilized to evaluate gamma-aminobutyric acid, glutamate, glutathione levels, and excitation/inhibition balance in the anterior cingulate cortex, posterior cingulate cortex, and occipital cortex of 39 neuromyelitis optica spectrum disorder patients and 41 healthy controls. Cognitive function was assessed using neurocognitive scales. Results showed decreased gamma-aminobutyric acid levels alongside increased glutamate, glutathione, and excitation/inhibition ratio in the anterior cingulate cortex and posterior cingulate cortex of neuromyelitis optica spectrum disorder patients. Specifically, within the posterior cingulate cortex of neuromyelitis optica spectrum disorder patients, decreased gamma-aminobutyric acid levels and increased excitation/inhibition ratio correlated significantly with anxiety scores, whereas glutathione levels predicted diminished executive function. The results suggest that neuromyelitis optica spectrum disorder patients exhibit dysregulation in the GABAergic and glutamatergic systems in their brains, where the excitation/inhibition imbalance potentially acts as a neuronal metabolic factor contributing to emotional disorders. Additionally, glutathione levels in the posterior cingulate cortex region may serve as predictors of cognitive decline, highlighting the potential benefits of reducing oxidative stress to safeguard cognitive function in neuromyelitis optica spectrum disorder patients.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

IL-36 antagonism blunts the proliferation and migration of oral squamous cell carcinoma cells.

IL-36 is known to mediate inflammation and fibrosis. Nevertheless, IL-36 signalling axis has also been implicated in cancer, although understanding of exact contribution of IL-36 to cancer progression is very limited, partly due to existence of multiple IL-36 ligands with agonistic and antagonistic function. Here we explored the role of IL-36 in oral squamous cell carcinoma (OSCC). Firstly, we analyzed expression of IL-36 ligands and receptor and found that the expression of IL-36γ was significantly higher in head and neck cancer (HNSCC) than that of normal tissues, and that the high expression of IL-36γ predicted poor clinical outcomes. Secondly, we investigated the direct effect of IL-36γ on OSCC cells and found that IL-36γ stimulated proliferation of OSCC cells with high expression of IL-36R expression. Interestingly, IL-36γ also promoted migration of OSCC cells with low to high IL-36R expression. Critically, both proliferation and migration of OSCC cells induced by IL-36γ were abrogated by anti-IL-36R mAb. Fittingly, RNA sequence analysis revealed that IL-36γ regulated genes involved in cell cycle and cell division. In summary, our results showed that IL-36γ can be a tumor-promoting factor, and targeting of IL-36R signalling may be a beneficial targeted therapy for patients with abnormal IL-36 signalling.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI.

RATIONALE AND OBJECTIVES: Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI. MATERIALS AND METHODS: First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression. RESULTS: The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression. CONCLUSION: The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

Mapping and Identifying Candidate Genes Enabling Cadmium Accumulation in Brassica napus Revealed by Combined BSA-Seq and RNA-Seq Analysis.

Rapeseed has the ability to absorb cadmium in the roots and transfer it to aboveground organs, making it a potential species for remediating soil cadmium (Cd) pollution. However, the genetic and molecular mechanisms underlying this phenomenon in rapeseed are still unclear. In this study, a 'cadmium-enriched' parent, 'P1', with high cadmium transport and accumulation in the shoot (cadmium root: shoot transfer ratio of 153.75%), and a low-cadmium-accumulation parent, 'P2', (with a cadmium transfer ratio of 48.72%) were assessed for Cd concentration using inductively coupled plasma mass spectrometry (ICP-MS). An F2 genetic population was constructed by crossing 'P1' with 'P2' to map QTL intervals and underlying genes associated with cadmium enrichment. Fifty extremely cadmium-enriched F2 individuals and fifty extremely low-accumulation F2 individuals were selected based on cadmium content and cadmium transfer ratio and used for bulk segregant analysis (BSA) in combination with whole genome resequencing. This generated a total of 3,660,999 SNPs and 787,034 InDels between these two segregated phenotypic groups. Based on the delta SNP index (the difference in SNP frequency between the two bulked pools), nine candidate Quantitative trait loci (QTLs) from five chromosomes were identified, and four intervals were validated. RNA sequencing of 'P1' and 'P2' in response to cadmium was also performed and identified 3502 differentially expressed genes (DEGs) between 'P1' and 'P2' under Cd treatment. Finally, 32 candidate DEGs were identified within 9 significant mapping intervals, including genes encoding a glutathione S-transferase (GST), a molecular chaperone (DnaJ), and a phosphoglycerate kinase (PGK), among others. These genes are strong candidates for playing an active role in helping rapeseed cope with cadmium stress. Therefore, this study not only sheds new light on the molecular mechanisms of Cd accumulation in rapeseed but could also be useful for rapeseed breeding programs targeting this trait.

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear. PURPOSE: To distinguish primary site of BM and identify the best DL models. STUDY TYPE: Retrospective. POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included. FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE). ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps. STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance. RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions. DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study.

Purpose: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). Methods: Two hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice. Conclusions: The nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC.

eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma.

BACKGROUND: Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (18F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA. METHODS: This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine 18F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays. RESULTS: Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with 18F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment. CONCLUSION: Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.

Comprehensive Analysis of Hexokinase 2 Immune Infiltrates and m6A Related Genes in Human Esophageal Carcinoma.

Background: Hexokinase 2 not only plays a role in physiological function of human normal tissues and organs, but also plays a vital role in the process of glycolysis of tumor cells. However, there are few comprehensive studies on HK2 in esophageal carcinoma (ESCA) needs further study. Methods: Oncomine, Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used to analyze the expression differences of HK2 in Pan-cancer and ESCA cohort, and to analyze the correlation between HK2 expression level and clinicopathological features of TCGA ESCA samples. GO/KEGG, GGI, and PPI analysis of HK2 was performed using R software, LinkedOmics, GeneMANIA and STRING online tools. The correlation between HK2 and ESCA immune infiltration was analyzed TIMER and TCGA ESCA cohort. The correlation between HK2 expression level and m6A modification of ESCA was analyzed by utilizing TCGA ESCA cohort. Results: HK2 is highly expressed in a variety of tumors, and its high expression level in ESCA is closely related to the weight, cancer stages, tumor histology and tumor grade of ESCA. The analysis results of GO/KEGG showed that HK2 was closely related to cell adhesion molecule binding, cell-cell junction, ameboidal-type cell migration, insulin signaling pathway, hif-1 signaling pathway, and insulin resistance. GGI showed that HK2 associated genes were mainly involved in the glycolytic pathway. PPI showed that HK2 was closely related to HK1, GPI, and HK3, all of which played an important role in tumor proliferation. The analysis results of TIMER and TCGA ESCA cohort indicated that the HK2 expression level was related to the infiltration of various immune cells. TCGA ESCA cohort analyze indicated that the HK2 expression level was correlated with m6A modification genes. Conclusion: HK2 is associated with tumor immune infiltration and m6A modification of ESCA, and can be used as a potential biological target for diagnosis and therapy of ESCA.

Planktonic Archaeal Ether Lipid Origins in Surface Waters of the North Pacific Subtropical Gyre.

Thaumarchaeota and Thermoplasmatota are the most abundant planktonic archaea in the sea. Thaumarchaeota contain tetraether lipids as their major membrane lipids, but the lipid composition of uncultured planktonic Thermoplasmatota representatives remains unknown. To address this knowledge gap, we quantified archaeal cells and ether lipids in open ocean depth profiles (0-200 m) of the North Pacific Subtropical Gyre. Planktonic archaeal community structure and ether lipid composition in the water column partitioned into two separate clusters: one above the deep chlorophyll maximum, the other within and below it. In surface waters, Thermoplasmatota densities ranged from 2.11 × 106 to 6.02 × 106 cells/L, while Thaumarchaeota were undetectable. As previously reported for Thaumarchaeota, potential homologs of archaeal tetraether ring synthases were present in planktonic Thermoplasmatota metagenomes. Despite the absence of Thaumarchaeota in surface waters, measurable amounts of intact polar ether lipids were found there. Based on cell abundance estimates, these surface water archaeal ether lipids contributed only 1.21 × 10-9 ng lipid/Thermoplasmatota cell, about three orders of magnitude less than that reported for Thaumarchaeota cells. While these data indicate that even if some tetraether and diether lipids may be derived from Thermoplasmatota, they would only comprise a small fraction of Thermoplasmatota total biomass. Therefore, while both MGI Thaumarchaeota and MGII/III Thermoplasmatota are potential biological sources of archaeal GDGTs, the Thaumarchaeota appear to be the major contributors of archaeal tetraether lipids in planktonic marine habitats. These results extend and confirm previous reports of planktonic archaeal lipid sources, and further emphasize the need for Thermoplasmatota cultivation, to better characterize the membrane lipid constituents of marine planktonic Thermoplasmatota, and more precisely define the sources and patterns of archaeal tetraether lipid distributions in marine plankton.

Programmed Size-Changeable Nanotheranostic Agents for Enhanced Imaging-Guided Chemo/Photodynamic Combination Therapy and Fast Elimination.

An ideal nanotheranostic agent should be able to achieve efficient tumor accumulation, retention, and fast elimination after its theranostic functions exhausts. However, there is an irreconcilable contradiction on optimum sizes for effective tumor retention and fast elimination. Herein, a programmed size-changeable nanotheranostic agent based on polyprodrug-modified iron oxide nanoparticles (IONPs) and aggregation-induced emission photosensitizer is developed for enhanced magnetic resonance imaging (MRI)-guided chemo/photodynamic combination therapy. The nano-sized theranostic agents with an initial diameter of about 90 nm can accumulate in tumor tissue through passive targeting. In the acidic tumor microenvironment, large aggregates of IONPs are formed, realizing enhanced tumor retention and MR signal enhancement. Under the guidance of MRI, light irradiation is applied to the tumor site for triggering the generation of reactive oxygen species and drug release. Moreover, after chemo/photodynamic combination therapy, the large-sized aggregates are re-dispersed into small-sized IONPs for fast elimination, reducing the risk of toxicity caused by long-term retention. Therefore, this study provides a promising size-changeable strategy for the development of nanotheranostic agents.

Synthesis, crystal structures, DNA interactions, and antitumor activity of two new dinuclear copper(ii) complexes with thiazole ligand.

Two new dinuclear copper(ii) complexes, [Cu(ambt)2(cnba)4] (1) and [Cu(ambt)2(clba)4] (2) were synthesized with 2-amino-6-methoxybenzothiazole (ambt) as the main ligand. The structures of the two complexes were characterized by single-crystal XRD. The binding between CT-DNA (calf thymus DNA) and the complexes was evaluated by viscometry, electronic absorption, and fluorescence spectroscopy, and the binding constants were calculated using the Stern-Volmer equation. The complexes were intercalatively bound to CT-DNA, and [Cu(ambt)2(clba)4] having a greater binding constant than [Cu(ambt)2(cnba)4]. The two complexes had better antitumor properties against HepG2 (human hepatocellular carcinoma), A549 (human lung carcinoma), and HeLa (human cervical carcinoma) tumor cell lines than their respective ligands and cisplatin. Furthermore, [Cu(ambt)2(clba)4] had a stronger inhibitory ability on the three types of tumor cells than [Cu(ambt)2(cnba)4], which is congruent with the binding power of the complexes with DNA. Flow cytometry revealed that [Cu(ambt)2(cnba)4] and [Cu(ambt)2(clba)4] could trigger apoptosis or necrosis, arrest the HepG2 cell cycles, and cause G0/G1-phase cells to accumulate.

Erratum.

Long noncoding RNA (lncRNA) colon cancer-associated transcript 2 (CCAT2) has been demonstrated to play an important role in diverse tumorigenesis. However, the biological function of lncRNAs in glioma is still unknown. In this study, we found that lncRNA CCAT2 was overexpressed in glioma tissues and cell lines and associated with tumor grade and size. Furthermore, patients with high levels of lncRNA CCAT2 had poorer survival than those with lower levels of lncRNA CCAT2. Knocking down lncRNA CCAT2 expression significantly suppressed the glioma cell growth, migration, and invasion, as well as induced early apoptosis of glioma cells in vitro. Moreover, lncRNA CCAT2 regulated epithelialmesenchymal transition (EMT)-associated gene expression. In conclusion, lncRNA CCAT2 plays an important role in glioma tumorigenesis and progression and may act as a potential biomarker for therapeutic strategy and prognostic prediction.

Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas.

PURPOSE: The purpose of this study was to explore the usefulness of diffusion kurtosis imaging (DKI) and molecular markers in predicting the prognosis of glioma patients. METHOD: Fifty-one patients with gliomas were examined by conventional MRI and DKI at 3.0 T before operation. The mean kurtosis (MK), mean diffusivity (MD), axial kurtosis (AK), and radial kurtosis (RK) values of tumors were measured and normalized to the contralateral normal-appearing white matter. The molecular markers of gliomas, including isocitrate dehydrogenase-1 (IDH1), α thalassemia/mental retardation syndrome x-linked (ATRX) and O6-methylguanine-DNA methyltransferase (MGMT), were immunohistochemically stained on the resected tumor tissues. Statistical methods, including the chi-square test, independent sample t-test, receiver operating characteristic curve analysis, Kaplan-Meier curve analysis, and Cox regression analysis were performed. RESULTS: The patients with lower MK, AK, RK, and higher MD values showed significantly better prognosis (P < 0.001). Survival time was better in glioma patients with IDH1 mutation (P < 0.01), ATRX loss of expression (P < 0.05), and MGMT negative expression (P < 0.05). However, among the groups of gliomas with IDH1 wild type, ATRX retention and those with MGMT positive expression, the patients with lower MK showed better outcome (P < 0.01). Cox multivariate regression analysis demonstrated that MK, RK values and ATRX retention could be used as independent prognostic risk factors, and high MK values had the highest risk for prognosis (HR = 65.288). CONCLUSIONS: Molecular markers and DKI parameters, especially MK values, can be used to effectively evaluate the prognosis of glioma patients.

Lemon essential oil ameliorates age-associated cognitive dysfunction via modulating hippocampal synaptic density and inhibiting acetylcholinesterase.

The lemon essential oil (LEO), extracted from the fruit of lemon, has been used to treat multiple pathological diseases, such as diabetes, inflammation, cardiovascular diseases, depression and hepatobiliary dysfunction. The study was designed to study the effects of LEO on cognitive dysfunction induced by Alzheimer's disease (AD). We used APP/PS1 double transgene (APP/PS1) AD mice in the experiment; these mice exhibit significant deficits in synaptic density and hippocampal-dependent spatial related memory. The effects of LEO on learning and memory were examined using the Morris Water Maze (MWM) test, Novel object recognition test, and correlative indicators, including a neurotransmitter (acetylcholinesterase, AChE), a nerve growth factor (brain-derived neurotrophic factor, BDNF), a postsynaptic marker (PSD95), and presynaptic markers (synapsin-1, and synaptophysin), in APP/PS1 mice. Histopathology was performed to estimate the effects of LEO on AD mice. A significantly lowered brain AChE depression in APP/PS1 and wild-type C57BL/6L (WT) mice. PSD95/ Synaptophysin, the index of synaptic density, was noticeably improved in histopathologic changes. Hence, it can be summarized that memory-enhancing activity might be associated with a reduction in the AChE levels and is elevated by BDNF, PSD95, and synaptophysin through enhancing synaptic plasticity.

MicroRNA-23a suppresses the apoptosis of inflammatory macrophages and foam cells in atherogenesis by targeting HSP90.

In previous study, we have found that microRNA-23a is down regulated in atherosclerotic tissues. Here we demonstrate that miR-23a directly binds to 3'UTR of HSP90 mRNA to suppress the expression of HSP90. To investigate the potential roles of miR-23a in macrophage, THP-1 macrophages were transfected with miR-23a mimics or inhibitors. Our results showed inflammatory factors IL-6 and MCP-1 concentrations in cell culture medium of macrophage and foam cell transfected with miR-23a mimics were decreased. Furthermore, we find that apoptosis of macrophage and foam cells transfected with miR-23a mimics were inhibited. Over expression of miR-23a in foam cells could reduced lipid intake and accumulation in foam cells. Meanwhile, we found that in inflammatory macrophages and foam cells transfected with miR-23a mimcs, HSP90 and NF-κB proteins are significantly decreased. Our results have suggested a promising and potential therapeutic target for atherosclerosis.

Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas.

PURPOSE: To retrospectively explore the utilization of MR diffusion kurtosis imaging (DKI) in predicting prognosis of the patients with high-grade gliomas. MATERIALS AND METHODS: Thirty-three consecutive patients with cerebral gliomas underwent pretreatment DKI and diffusion-weighted imaging examination on a 3.0-T MR scanner. Diffusion parameters, including conventional tensor parameters, kurtosis metrics (mean kurtosis [MK], radial kurtosis [AK], and axial kurtosis [RK]), and minimum apparent diffusion coefficient (minADC), were obtained and normalized to the contralateral normal-appearing white matter. Correlations among each diffusion parameter and overall survival were analyzed by a Spearman method. The diagnostic efficiency of each parameter in predicting survival for patients with high-grade gliomas was assessed by a receiver operating characteristic curve. The favorable prognostic imaging biomarkers were further analyzed by using a Kaplan-Meier method with log-rank test. RESULTS: In 33 patients, 17 patients reached overall survival >15 months (long survival group), whereas 16 showed overall survival <15 months (short survival group). Negative correlations between kurtosis metrics (MK, AK, and RK) and overall survival were obtained by using Spearman analysis (r = -0.63, -0.57, and -0.61, respectively, all P < 0.01), whereas minADC was positively correlated with overall survival (r = 0.56, P < 0.01). The kurtosis parameters of the long survival group were significantly lower than that of the short survival group (P < 0.001), while the minADC of the long survival group was significantly higher than that of the short survival group (P = 0.002). Among these diffusion parameters, the optimal cut-off value of MK (0.688) provided the best combination of sensitivity (93.75%) and specificity (76.47%) for differentiation of patients with long survival from those with short survival. High kurtosis metrics and low minADC were significant predictors of poor outcome. (P < 0.05). CONCLUSION: Both kurtosis metrics and minADC have the potential to predict survival for the patients with high-grade gliomas. The preoperative kurtosis parameters, especially MK, can be taken as a preoperative prognostic biomarker to predict prognosis in patients with high-grade gliomas.