Relationship between nonalcoholic fatty liver disease and bone mineral density in elderly Chinese.

OBJECTIVE: As our society grows older, both bone loss and fatty liver have become important issues. However, the relationship between reduced bone mineral density and fatty liver remains controversial. The purpose of this study was to investigate the relationship between nonalcoholic fatty liver disease and less bone mineral density in the ankles of Chinese people over 65. METHODS: The research included 716 older adults over the age of 65 who underwent a comprehensive health screening. A logistic regression model was used to investigate the relationship between nonalcoholic fatty liver disease (NAFLD) and ankle bone mineral density. RESULTS: A logistic regression model was used to analyze the odds ratios of reduced bone mineral density between patients with fatty liver and controls. In the adjusted model, adjustments were made for age, sex, systolic blood pressure, diastolic blood pressure, overweight rate, history of hypertension, history of diabetes, history of smoking, history of alcohol consumption, fasting glucose, hemoglobin, urea, creatinine, triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, waist circumference, total protein, albumin, and globulin. The adjusted OR (aOR) for reduced bone mineral density was 0.66 [95% confidence interval (CI) = 0.45-0.97, P = 0.034 < 0.05]. In subgroup analysis by age, sex, and BMI, women, age > 75 years, and BIM < 25 were statistically significant. CONCLUSION: This study suggested that NAFLD is associated with a reduced risk of reduced heel bone mass. More research needs to be done to back up the results of this study and look into possible problems.

miR-1-3p Inhibits Osteosarcoma Cell Proliferation and Cell Cycle Progression While Promoting Cell Apoptosis by Targeting CDK14 to Inactivate Wnt/Beta-Catenin Signaling.

Osteosarcoma (OS) is a common bone malignancy and is diagnosed frequently in children and young adults. According to previous RNA sequencing, miR-1-3p is downregulated in OS clinical samples. Nevertheless, the functions of miR-1-3p in OS cell process and the related mechanism have not been revealed yet. In the current study, miR-1-3p expression in OS tissues and cells were evaluated using quantitative polymerase chain reaction. CCK-8 assays were conducted to measure OS cell viability in response to miR-1-3p overexpression. Colony forming assays and EdU staining were conducted for measurement of cell proliferation, and flow cytometry analysis was performed to determine cell apoptosis and cell cycle progression. Protein levels of apoptotic markers, beta-catenin, and Wnt downstream targets were quantified using western blotting. The binding relation between miR-1-3p and cyclin dependent kinase 14 (CDK14) was validated utilizing luciferase reporter assays. Experimental results revealed that miR-1-3p expression was decreased in OS tissues and cells. Additionally, miR-1-3p inhibited cell proliferation and cell cycle progression while enhancing OS cell apoptosis. Moreover, miR-1-3p directly targeted CDK14 and inversely regulated CDK14 expression in OS cells. Furthermore, miR-1-3p inactivated the Wnt/beta-catenin signaling. CDK14 overexpression partially rescued the inhibitory impact of miR-1-3p on OS cell growth. Overall, miR-1-3p inhibits OS cell proliferation and cell cycle progression while promoting cell apoptosis by targeting CDK14 and inactivating the Wnt/beta-catenin signaling.

Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.

Background: Accurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility. Materials and methods: Patients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms-two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])-were selected for training. Meanwhile, a multivariate Cox proportional hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into training and testing datasets at a ratio of 7:3. Hyperparameter tuning was conducted through a 1000-repeated random search with 5-fold cross-validation on the training dataset. The model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-, 3-, 5- and 10-year survival was evaluated using receiver operating characteristic curves (ROC), calibration curves, and the area under the ROC curves (AUC). Results: A total of 3145 patients were finally enrolled in our study. The mean age at diagnosis was 52 ± 18 years, 1662 of the 3145 patients were male (53%), and mean survival time was 83 ± 67 months. Two deep learning models outperformed the RSF and classical CoxPH models, with the C-index on test datasets achieving values of 0.832 (DeepSurv) and 0.821 (NMTLR). The DeepSurv model produced better accuracy and calibrated survival estimates in predicting 1-, 3- 5- and 10-year survival (AUC:0.895-0.937). We deployed the DeepSurv model as a web application for use in clinical practice; it can be accessed through https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py. Conclusions: Time-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.

Identification and validation of a ferroptosis-related gene signature for predicting survival in skin cutaneous melanoma.

PURPOSE: Ferroptosis plays a crucial role in the initiation and progression of melanoma. This study developed a robust signature with ferroptosis-related genes (FRGs) and assessed the ability of this signature to predict OS in patients with skin cutaneous melanoma (SKCM). METHODS: RNA-sequencing data and clinical information of melanoma patients were extracted from TCGA, GEO, and GTEx. Univariate, multivariate, and LASSO regression analyses were conducted to identify the gene signature. A 10 FRG signature was an independent and strong predictor of survival. The predictive performance was assessed using ROC curve. The functions of this gene signature were assessed by GO and KEGG analysis. The statuses of low-risk and high-risk groups according to the gene signature were compared by GSEA. In addition, we investigated the possible relationship of FRGs with immunotherapy efficacy. RESULTS: A prognostic signature with 10 FRGs (CYBB, IFNG, FBXW7, ARNTL, PROM2, GPX2, JDP2, SLC7A5, TUBE1, and HAMP) was identified by Cox regression analysis. This signature had a higher prediction efficiency than clinicopathological features (AUC = 0.70). The enrichment analyses of DEGs indicated that ferroptosis-related immune pathways were largely enriched. Furthermore, GSEA showed that ferroptosis was associated with immunosuppression in the high-risk group. Finally, immune checkpoints such as PDCD-1 (PD-1), CTLA4, CD274 (PD-L1), and LAG3 were also differential expression in two risk groups. CONCLUSIONS: The 10 FRGs signature were a strong predictor of OS in SKCM and could be used to predict therapeutic targets for melanoma.

The correlation between dynamic functional architecture and response to electroconvulsive therapy combined with antipsychotics in schizophrenia.

Attempts to determine why some patients respond to electroconvulsive therapy (ECT) are valuable in schizophrenia. Schizophrenia is associated with aberrant dynamic functional architecture, which might impact the efficacy of ECT. We aimed to explore the relationship between pre-treatment temporal variability and ECT acute efficacy. Forty-eight patients with schizophrenia and 30 healthy controls underwent functional magnetic resonance imaging to examine whether patterns of temporary variability of functional architecture differ between high responders (HR) and low responders (LR) at baseline. Compared with LR, HR exhibited significantly abnormal temporal variability in right inferior front gyrus (IFGtriang.R), left temporal pole (TPOsup.L) and right middle temporal gyrus (MTG.R). In the pooled patient group, ∆PANSS was correlated with the temporal variability of these regions. Patients with schizophrenia with a distinct dynamic functional architecture appear to reveal differential response to ECT. Our findings provide not only an understanding of the neural functional architecture patterns that are found in schizophrenia but also the possibility of using these measures as moderators for ECT selection.

Quantitative evaluation of electrical conductivity inside stress corrosion crack with electromagnetic NDE methods.

This paper presents a comparison of studies on the local distributed electrical conductivity in stress corrosion crack (SCC) from signals of eddy current testing (ECT) and direct current potential drop (DCPD) aiming to improve SCC sizing accuracy when using electromagnetic non-destructive testing (NDT) methods. Experimental setups of ECT and DCPD were established, respectively, to collect measurement signals due to artificial SCCs in a plate of austenitic stainless steel. The local conductivity in the SCC region was reconstructed from the feature parameters extracted from the measured ECT and DCPD signals through inverse analyses. The inversion strategies for ECT and DCPD, each including an efficient forward simulation and an optimization scheme, were introduced from the viewpoint of conductivity reconstruction. Inversion results obtained from the measured ECT and DCPD signals showed the consistent trend which proved the validity of the predicted electrical conductivity indirectly. It is clarified that the electrical conductivity in a SCC is relatively high at the crack tip area and may become as high as 17% of that of the base material. These results provide a good reference to enhance the sizing accuracy of SCC with an electromagnetic NDT method such as ECT by updating the conductive crack model based on the results of this work. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.

Altered functional connectivity of the amygdala in Crohn's disease.

Crohn's disease (CD), a chronic inflammatory bowel disease, involved in brain structural and functional changes, including the amygdala. Amygdala is a key structure in the limbic system and its related circuits are implicated in processing of emotion, pain and sensory. However, limited study of the amygdala is elucidated in CD. This study mainly investigated altered functional connectivity (FC) of the amygdala in CD patients during resting-state. Magnetic resonance imaging scans were acquired from 42 CD patients and 35 healthy controls (HCs). Whole amygdala bilaterally were selected as regions of interest (ROIs). Voxel-based morphometry and FC methods were applied to investigate the differences of structure or intrinsic connectivity of the amygdala between the two groups, separately. Pearson correlations were performed to explore relationships between the clinical characteristics and neuroimaging findings in CD patients. Based on the whole amygdala bilaterally as ROIs, compared with HCs, CD patients showed no statistical differences of grey matter destiny but exhibited decreased FC between the amygdala and insula, parahippocampus, as well as anterior middle cingulate cortex/dorsal anterior cingulate cortex. CD patients had negative correlation between the disease duration and amygdala-insula connectivity. In the patient group, patients with higher anxiety or depression scores revealed increased FC of the amygdala with thalamus and orbitofrontal cortex. Our results reveal that aberrant FC of the amygdala may be involved in processing of visceral pain and sensation, and emotion in CD. These findings may further enhance the understanding of neural mechanisms of CD.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging.

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.