Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder.

Background: In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods: A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results: The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions: The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

The clinical and imaging features of cerebrotendinous xanthomatosis: A case report and review of the literature.

RATIONALE: Cerebrotendinous xanthomatosis (CTX) is a rare autosomal recessive lipid deposition disorder characterized by systemic signs and neurological dysfunction. The radiological features of CTX are infrequently summarized in the literature. PATIENT CONCERNS: We described a 40-year-old male patient who repeatedly engaged in wrestling matches and presented with progressive difficulty in walking and reduced balance with egg-sized, hard, smooth, and painless masses in both ankles. DIAGNOSIS: Neuroimaging examination showed abnormalities both supra- and infratentorially. Bilateral ankle joint magnetic resonance imaging showed bilateral xanthomata of the Achilles tendon. The diagnosis was confirmed by the detection of a sterol 27-hydroxylase gene mutation. INTERVENTIONS: The patient was treated with chenodeoxycholic acid (250 mg 3 times per day). OUTCOMES: To date, the patient's bilateral xanthomas of the Achilles tendon have begun to diminish, and his neurological impairment has not deteriorated further but has not yet improved. LESSONS: We report a rare case of CTX and summarize the clinical and imaging features of this disease. Our findings suggest that the abnormal signals in the dentate nucleus or a long spinal cord lesion involving the central and posterior cord, combined with tendon xanthoma, are important clues for the diagnosis of CTX.

CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes.

PURPOSE: The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes. METHOD: Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed. RESULTS: The two texture parameters with the top two AUC values were - 333-7 Correlation (AUC = 0.772) and 45-7 Entropy (AUC = 0.753) in the corticomedullary phase, 333-4 Correlation (AUC = 0.832) and 45-7 Entropy (AUC = 0.841) in the nephrographic phase, and 135-7 Entropy (AUC = 0.858) and - 333-1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit. CONCLUSIONS: Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.

The role of iron in Parkinson's disease monkeys assessed by susceptibility weighted imaging and inductively coupled plasma mass spectrometry.

Mounting evidences indicated that elevated iron levels in the substantia nigra (SN) have been concerned as the underlying mechanisms of neurodegenerative diseases, including Parkinson's disease (PD). The present study used the 1-Methyl-4-phenyl-1, 2, 3, 6 -tetrahydropyridine (MPTP)-treated cynomolgus monkeys for PD to evaluate the usability of SWI for assessing iron deposition in the cerebral nuclei of PD. The results showed that susceptibility-weighted imaging (SWI) phase values of the ipsilateral (MPTP-lesion side) SN of MPTP-treated monkeys were lower than those in the contralateral SN of MPTP-treated monkeys and the same side of Control monkeys, suggesting that iron deposition were elevated in the affected side SN of MPTP-treated monkeys. Whereas MPTP has not effects on the SWI phase values in other detected brain regions of monkeys, including red nucleus (RN), putamen (PUT) and caudate nucleus (CA). Furthermore, ICP-MS results showed that MPTP increased the iron levels in MPTP injection side, but no in the ipsilateral striatum. Additionally, MPTP treatment did not affect the calcium and manganese levels in the detected brain regions of monkeys. However, Pearson correlation analysis results indicated that there were not relationship between SWI phase values in MPTP-lesion side of SN with the behavioral score, tyrosine hydroxylase (TH)-positive cells number and iron levels in the MPTP-lesion side of midbrain. Taken together, the results confirm the involvement of SN iron accumulations in the MPTP-treated monkey models for PD, and indirectly verify the usability of SWI for the measurement of iron deposition in the cerebral nuclei of PD.

Targeting XIST induced apoptosis of human osteosarcoma cells by activation of NF-kB/PUMA signal.

The long noncoding RNA X-inactive specific transcript (XIST) plays vital roles in tumor progression. However, the underlying mechanisms remain unclear. This study investigated the effects and mechanisms of targeting XIST on osteosarcoma (OS) cells in vitro and in vivo. We used shRNA to knockdown XIST to evaluate cell growth and apoptosis in U2OS cells in vitro and xenograft formation in vivo. An observed relationship between XIST and the p53 upregulated modulator of apoptosis (PUMA) and nuclear factor-kappa B (NF-kB) pathway was further explored by using small interfering RNA (siRNA). Our results showed that suppression of XIST by short hairpin RNA (shRNA) impeded U2OS cell growth, induced apoptosis and lessened OS xenograft tumor growth. Targeting XIST increased NF-kB-dependent PUMA upregulation in U2OS cells. Upregulation of PUMA is correlated with suppression of XIST-induced apoptosis in U2OS cells. Therefore, inhibition of XIST could promote U2OS cell death via activation of NF-kB/PUMA pathways.

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

BACKGROUND: The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. PATIENTS AND METHODS: Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. RESULTS: Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). CONCLUSION: Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance.