A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies. OBJECTIVE: To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD. METHODS: We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD. RESULTS: Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes. CONCLUSIONS: Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

Time-series analysis of meteorological factors and emergency department visits due to dog/cat bites in Jinshan area, China.

Background: Meteorological factors play an important role in human health. Clarifying the occurrence of dog and cat bites (DCBs) under different meteorological conditions can provide key insights into the prevention of DCBs. Therefore, the objective of the study was to explore the relationship between meteorological factors and DCBs and to provide caution to avoid the incidents that may occur by DCBs. Methods: In this study, data on meteorological factors and cases of DCBs were retrospectively collected at the Shanghai Climate Center and Jinshan Hospital of Fudan University, respectively, in 2016-2020. The distributed lag non-linear and time series model (DLNM) were used to examine the effect of meteorological elements on daily hospital visits due to DCBs. Results: A total of 26,857 DCBs were collected ranging from 1 to 39 cases per day. The relationship between ambient temperature and DCBs was J-shaped. DCBs were positively correlated with daily mean temperature (rs = 0.588, P < 0.01). The relative risk (RR) of DCBs was associated with high temperature (RR = 1.450; 95% CI [1.220-1.722]). Female was more susceptible to high temperature than male. High temperature increased the risk of DCBs. Conclusions: The extremely high temperature increased the risk of injuries caused by DCBs, particularly for females. These data may help to develop public health strategies for potentially avoiding the occurrence of DCBs.

Prognostic Value and Potential Mechanism of MTFR2 in Lung Adenocarcinoma.

Mitochondrial fission regulator 2 (MTFR2) belongs to the MTFR1 family, which plays a crucial role in regulating oxidative phosphorylation. Recent studies indicate that it also participates in cancer carcinogenesis and development; however, the clinical significance of MTFR2 in lung adenocarcinoma has not been fully confirmed. Our current study investigated the relationships between clinical characteristics and MTFR2 expression based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GSE31210) dataset, and clinical histopathological sample cohort. In addition, Kaplan-Meier and Cox regression analyses were additionally performed to evaluate the association between MTFR2 expression and patient survival. Gene set enrichment analysis (GESA) was conducted to spot possible pathways associated with MTFR2. Moreover, a single-sample GESA (ssGESA) was performed to evaluate the association between MTFR2 expression and immune cell infiltration. Cell colony formation assay, CCK-8 assay, cell cycle assay, and transwell assay were performed to verify the cell proliferation, migration, and invasion abilities after interfering with MTFR2 in lung cancer cells. Western blot assay was applied to identify the underlying protein levels. The results indicated that the elevated MTFR2 expression in lung adenocarcinoma samples correlated with T stage (P < 0.001), N stage (P = 0.005), M stage (P = 0.015), pathological stage (P = 0.002), and TP53 status (P < 0.001). Patients with a higher MTFR2 expression correlated with poorer overall survival (P < 0.01) and progression-free survival (P = 0.002). Knockdown of MTFR2 inhibited cell proliferation, migration, and invasion via AKT-cyclin D1 signaling and EMT pathways. Moreover, MTFR2 expression significantly positively correlated with Th2 cells (P < 0.001). Taken together, MTFR2 could serve as a novel prognostic indicator and therapeutic target for lung adenocarcinoma.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

BACKGROUND: Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS: CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS: The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION: The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer.

BACKGROUND: Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method. AIM: To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC. METHODS: A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging. RESULTS: The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858). CONCLUSION: MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.

The CPNE Family and Their Role in Cancers.

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite significant advances in cancer research and treatment, the overall prognosis of lung cancer patients remains poor. Therefore, the identification for novel therapeutic targets is critical for the diagnosis and treatment of lung cancer. CPNEs (copines) are a family of membrane-bound proteins that are highly conserved, soluble, ubiquitous, calcium dependent in a variety of eukaryotes. Emerging evidences have also indicated CPNE family members are involved in cancer development and progression as well. However, the expression patterns and clinical roles in cancer have not yet been well understood. In this review, we summarize recent advances concerning CPNE family members and provide insights into new potential mechanism involved in cancer development.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.

OBJECTIVE: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T1WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD. RESULTS: Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798. CONCLUSION: The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

A comparative analysis between low-dose-rate brachytherapy and external beam radiation therapy for low- and intermediate-risk prostate cancer in Asian men.

OBJECTIVE: To report the long-term clinical outcomes of low-risk (LR) and intermediate-risk (IR) prostate cancer patients treated with low-dose-rate brachytherapy (LDR-BT) and external beam radiation therapy (EBRT). PATIENTS AND METHODS: Men with biopsy-proven low- and intermediate-risk prostate cancer received EBRT and LDR-BT in an Asian academic center from 2000 to 2019 were reviewed. Kaplan-Meier survival analysis was performed to compare biochemical failure-free survival (bFFS) and overall survival (OS) between LDR and EBRT in the low- and intermediate-risk cohorts. RESULTS: 642 patients (521 EBRT and 121 LDR-BT) with low- and intermediate-risk prostate cancer were included for analysis. In the intermediate-risk group, 5- and 10-year bFFS was 96%, 89% and 86%, 61% for LDR-BT and EBRT, respectively. LDR-BT was associated with a statistically significant improvement of bFFS in the intermediate-risk cohort (HR 2.7, p = 0.02). In the low-risk cohort, no difference of bFFS was found between LDR-BT and EBRT (HR 1.9, p = 0.08). Hormone therapy was more common in EBRT than LDR-BT for intermediate-risk group (71% versus 44%, p < 0.05). Prostate cancer-specific mortality was low in both EBRT (1%) and LDR-BT (2%) cohorts. No significant difference in OS was found between LDR-BT and EBRT in low- and intermediate-risk group (HR 2.1, p = 0.2 and HR = 1.7, p = 0.3). CONCLUSION: In our retrospective study, LDR-BT is associated with superior bFFS compared with EBRT in Asian men with intermediate-risk prostate cancer.

Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis.

This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross-sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast-enhanced T1, T2, and T2 FLAIR. Each sequence's Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.

MRPL42 is activated by YY1 to promote lung adenocarcinoma progression.

Mammalian mitochondrial ribosomal proteins are a group of protein factors encoded by nuclear genes, responsible for the synthesis of proteins in mitochondria. As a member of mitochondrial ribosomal proteins, MRPL42 (mitochondrial ribosomal protein L42) belongs to 28S and 39S subunits. The current literature showed that its role in lung adenocarcinoma (LUAD) was not clear. We found that MRPL42 was highly expressed in early-stage LUAD tissues and cell lines, and remarkably related to the prognosis of patients. Knockdown of MRPL42 could reduce the proliferation and colonization, promote cell cycle arrest in G1/S phase, and weaken the migration and invasion ability of LUAD cells in vitro. Moreover, depletion of MRPL42 also inhibited tumor growth in vivo. Bioinformatics analysis found that YY1 may bind to the promoter region upstream of the MRPL42 gene to promote the transcription of MRPL42, which was verified by the ChIP and Dual luciferase reporter assay. QRT-PCR confirmed that knocking down YY1 could attenuate the expression of MRPL42. In summary, MRPL42 acts as an oncogene in LUAD, and its expression level is regulated by YY1.

Metformin restores hippocampal neurogenesis and learning and memory via regulating gut microbiota in the obese mouse model.

Numerous studies have shown that over-nutritional obesity may lead to pre-diabetes, type 2 diabetes and cognitive decline. As the degree of metabolic disorders increases, the cognitive decline is getting worse. However, the cellular events that cause this cognitive dysfunction is yet to be clarified. We used a high-fat diet (HFD) consumption-induced obesity mouse model to test the effects of metformin on the hippocampal neurogenesis and learning and memory abilities of obese mice. 5-Bromo-2'-deoxyuridine (BrdU) labelling and retrovirus labeling were applied to detect hippocampal newborn neurons. Behavioral experiments were used to detect learning and memory abilities of mice. 16S rRNA gene sequencing was performed to detect the composition of gut microbiota. The positron emission tomography (PET) was conducted to detect the energy metabolism activity of different mouse brain regions. Our results reveal that metformin restores the impairment of neurogenesis in the dentate gyrus and finally prevents the cognitive decline of the obese mice. Moreover, the therapeutic effects of metformin are achieved by regulating the composition of gut microbiota of mice, which may inhibit microglia activation and neuroinflammation in the brain of obese mice. This study suggests that metformin may be taken as a promising candidate for the intervention of cognitive decline related to imbalance of gut microbiota caused by obesity.

RPB5-mediating protein promotes the progression of non-small cell lung cancer by regulating the proliferation and invasion.

BACKGROUND: This study aimed to investigate the relationship between RNA polymerase II subunit 5 (RPB5)-mediating protein (RMP) and clinicopathological characteristics of non-small cell lung cancer (NSCLC) patients by measuring the expression level of RMP in human NSCLC tissues and cell lines. At the same time, we studied the impact of RMP on the biological function of cancer, providing strong support for gene targeted therapy of NSCLC. METHODS: Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot were used to determine the expression levels of messenger (m)RNA and protein in NSCLC cell lines and tissues. Cell counting kit 8 (CCK8) assay and flow cytometry were selected to detect cell proliferation, cycle and apoptosis. The wound healing assay was chosen to detect the migration and invasion ability of cells. The xenograft model was performed to study the function of RMP in vivo. Immunohistochemical (IHC) staining showed the levels of RMP, Bcl-2, Bax and caspase-3. RESULTS: First, mRNA and protein levels of RMP were relatively overexpressed in NSCLC cells. Compared with the corresponding normal tissues, the mRNA and protein levels of RMP were significantly higher in human NSCLC tissues. Concurrently, we found that the expression of RMP was related to the status of lymph nodes (LNs) in cancer tissues and T stage. Then, RMP overexpression promoted the proliferation of A549. At the same time, RMP provided A549 cells the ability to resist chemotherapy and radiotherapy; when A549 cells were treated with gefitinib and radiation, RMP reduced apoptosis. We also found that RMP can protect A549 from G2 block caused by radiation. Over-irradiated RMP-overexpressed A549 cells had lower Bcl2-associated X protein (Bax) levels and higher B-cell lymphoma 2 (Bcl-2) levels. The migration and invasion ability of A549 cells was increased by RMP. Finally, RMP can promote tumor growth by increasing Bcl-2 levels and decreasing Bax and caspase-3 levels in the xenograft model. CONCLUSIONS: There is potential for RMP to develop into a diagnostic and therapeutic target for NSCLC.

Loss of ferroportin induces memory impairment by promoting ferroptosis in Alzheimer's disease.

Iron homeostasis disturbance has been implicated in Alzheimer's disease (AD), and excess iron exacerbates oxidative damage and cognitive defects. Ferroptosis is a nonapoptotic form of cell death dependent upon intracellular iron. However, the involvement of ferroptosis in the pathogenesis of AD remains elusive. Here, we report that ferroportin1 (Fpn), the only identified mammalian nonheme iron exporter, was downregulated in the brains of APPswe/PS1dE9 mice as an Alzheimer's mouse model and Alzheimer's patients. Genetic deletion of Fpn in principal neurons of the neocortex and hippocampus by breeding Fpnfl/fl mice with NEX-Cre mice led to AD-like hippocampal atrophy and memory deficits. Interestingly, the canonical morphological and molecular characteristics of ferroptosis were observed in both Fpnfl/fl/NEXcre and AD mice. Gene set enrichment analysis (GSEA) of ferroptosis-related RNA-seq data showed that the differentially expressed genes were highly enriched in gene sets associated with AD. Furthermore, administration of specific inhibitors of ferroptosis effectively reduced the neuronal death and memory impairments induced by Aß aggregation in vitro and in vivo. In addition, restoring Fpn ameliorated ferroptosis and memory impairment in APPswe/PS1dE9 mice. Our study demonstrates the critical role of Fpn and ferroptosis in the progression of AD, thus provides promising therapeutic approaches for this disease.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter.

PURPOSE: This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). METHODS: PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T1 -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. RESULTS: Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600. CONCLUSION: Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer.

Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

[Application Values of Gadolinium-ethoxybenzyl Diethylenetriaminepentaacetic Acid Enhanced Magnetic Resonance Imaging-based Radiomics in the Quantitative Assessment of Liver Reserve Function of Patients with Liver Cirrhosis].

Objective To evaluate the correlation between the radiomics signature of hepatobiliary phase imaging of gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid(Gd-EOB-DTPA)enhanced magnetic resonance imaging(MRI)and Child-Pugh of liver cirrhosis,establish nomogram prediction model,and assess the predictive value of quantitative assessment of liver reserve function of patients with liver cirrhosis. Methods One hundred patients with liver cirrhosis who met the inclusion criteria were divided into 52 patients with Child-Pugh grade A and 48 patients with Child-Pugh grade B+C according to Child-Pugh classification criteria,and were randomly divided into training set and test set at a proportion of 7∶3.The AK software was used to extract the imaging features of the Gd-EOB-DTPA-enhanced MRI hepatobiliary images of the patients in the training set,and the least absolute shrinkage and selection operator feature selection algorithm was used to reduce the dimension of the data,select the features,and construct the radiomics tags.According to the radiomics label Rad-score,a line chart(nomogram)prediction model was established to predict the Child-Pugh B+C level of liver reserve function.The model was applied to the training set and test set respectively,and the diagnostic efficiency was quantitatively evaluated by receiver operating characteristic(ROC)curve. Results After dimension reduction and screening of 396 texture feature parameters extracted by AK software,7 image feature parameters were obtained.According to the above characteristics,the radiomics tag Rad-score was constructed and the nomogram prediction model was created.The differences of Rad-score scores between Child-Pugh A and Child-Pugh B+C groups in training set and test set were statistically analyzed by Wilcoxon rank sum test(P=0.000, P=0.001).The diagnostic efficacy of nomogram prediction model for predicting Child-Pugh B+C grade of liver reserve function in the ROC curve of training set and test set was 0.88 and 0.86 respectively. Conclusions The nomogram prediction model created according to the radiomics tag Rad-score of patients with liver cirrhosis with different liver reserve functions can be used as a more accurate and reliable auxiliary detection tool for liver reserve function.It provides a new means for clinicians to evaluate liver reserve function more accurately.

Correcting abnormalities in miR-124/PTPN1 signaling rescues tau pathology in Alzheimer's disease.

MicroRNAs have been implicated in diverse physiological and pathological processes. We previously reported that aberrant microRNA-124 (miR-124)/non-receptor-type protein phosphatase 1 (PTPN1) signaling plays an important role in the synaptic disorders associated with Alzheimer's disease (AD). In this study, we further investigated the potential role of miR-124/PTPN1 in the tau pathology of AD. We first treated the mice with intra-hippocampal stereotactic injections. Then, we used quantitative real-time reverse transcription PCR (qRT-PCR) to detect the expression of microRNAs. Western blotting was used to measure the level of PTPN1, the level of tau protein, the phosphorylation of tau at AD-related sites, and alterations in the activity of glycogen synthase kinase 3ß (GSK-3ß) and protein phosphatase 2 (PP2A). Immunohistochemistry was also used to detect changes in tau phosphorylation levels at AD-related sites and somadendritic aggregation. Soluble and insoluble tau protein was separated by 70% formic acid (FA) extraction to examine tau solubility. Finally, behavioral experiments (including the Morris water maze, fear conditioning, and elevated plus maze) were performed to examine learning and memory ability and emotion-related behavior. We found that artificially replicating the abnormalities in miR-124/PTPN1 signaling induced AD-like tau pathology in the hippocampus of wild-type mice, including hyperphosphorylation at multiple sites, insolubility and somadendritic aggregation, as well as learning/memory deficits. We also found that disruption of miR-124/PTPN1 signaling was caused by the loss of RE1-silencing transcription factor protein, which can be initiated by Aß insults or oxidative stress, as observed in the brains of P301S mice. Correcting the deregulation of miR-124/PTPN1 signaling rescued the tau pathology and learning/memory impairments in the P301S mice. We also found that miR-124/PTPN1 abnormalities induced activation of glycogen synthase kinase 3 (GSK-3) and inactivation of protein phosphatase 2A (PP2A) by promoting tyrosine phosphorylation, implicating an imbalance in tau kinase/phosphatase. Thus, targeting the miR-124/PTPN1 signaling pathway is a promising therapeutic strategy for AD.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults.

BACKGROUND: White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. PURPOSE: To develop and validate a radiomics nomogram for the prediction of WMH progression. STUDY TYPE: Retrospective. POPULATION: Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. FIELD STRENGTH/SEQUENCE: T1 -fluid attenuated inversion recovery images were acquired using a 3T scanner. ASSESSMENT: WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. STATISTICAL TESTS: A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed. RESULTS: The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. DATA CONCLUSION: This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer.

BACKGROUND: In pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required. PURPOSE: To develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice. STUDY TYPE: Retrospective. POPULATION: We enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126). FIELD STRENGTH/SEQUENCE: 3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w). ASSESSMENT: ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated. STATISTICAL TESTS: The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit. RESULTS: The AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort). DATA CONCLUSION: The radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.