Interactions Among Non-Coding RNAs and mRNAs in the Trigeminal Ganglion Associated with Neuropathic Pain.

Background: Recent studies have demonstrated the contribution of non-coding RNAs (ncRNAs) to neuropathic pain. However, the expression profile of ncRNAs in the trigeminal ganglion (TG) and their functional mechanism underlying trigeminal neuropathic pain are still unclear. Methods: In the present study, the trigeminal neuropathic pain model induced by chronic constriction injury of the infraorbital nerve (CCI-ION) was used to study the expression profile and potential regulatory mechanism of miRNAs, lncRNAs, circRNAs, and mRNAs in the TG by RNA-sequencing (RNA-seq) and bioinformatics analysis. CCI-ION mice suffered from mechanical allodynia from 3 days to 28 days after surgery. Results: The RNA-seq results discovered 67 miRNAs, 216 lncRNAs, 14 circRNAs, 595 mRNAs, and 421 genes were differentially expressed (DE) in the TG of CCI-ION mice 7 days after surgery. And 39 DEGs were known pain genes. Besides, 5 and 35 pain-related DE mRNAs could be targeted by 6 DE miRNAs and 107 DE lncRNAs, respectively. And 23 pain-related DEGs had protein-protein interactions (PPI) with each other. GO analysis indicated membrane-related cell components and binding-related molecular functions were significantly enriched. KEGG analysis showed that nociception-related signaling pathways were significantly enriched for DE ncRNAs and DEGs. Finally, the competing endogenous RNA (ceRNA) regulatory network of DE lncRNA/DE circRNA-DE miRNA-DE mRNA existed in the TG of mice with trigeminal neuropathic pain. Conclusion: Our findings demonstrate ncRNAs are involved in the development of trigeminal neuropathic pain, possibly through the ceRNA mechanism, which brings a new bright into the study of trigeminal neuropathic pain and the development of novel treatments targeting ncRNAs.

A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images.

Early screening of PDAC (pancreatic ductal adenocarcinoma) based on plain CT (computed tomography) images is of great significance. Therefore, this work conducted a radiomics-aided diagnosis analysis of PDAC based on plain CT images. We explored a novel MSTA (multiresolution-statistical texture analysis) architecture to extract texture features and built machine learning models to classify PDACs and HPs (healthy pancreases). We also performed significance tests of differences to analyze the relationships between histopathological characteristics and texture features. The MSTA architecture originates from the analysis of histopathological characteristics and combines multiresolution analysis and statistical analysis to extract texture features. The MSTA architecture achieved better experimental results than the traditional architecture that scales the coefficient matrices of the multiresolution analysis. In the validation of the classifications, the MSTA architecture achieved an accuracy of 81.19% and an AUC (area under the ROC (receiver operating characteristic) curve) of 0.88 (95% confidence interval: 0.84-0.92). In the test of the classifications, it achieved an accuracy of 77.66% and an AUC of 0.79 (95% confidence interval: 0.71-0.87). Moreover, the significance tests of differences showed that the extracted texture features may be relevant to the histopathological characteristics. The MSTA architecture is beneficial for the radiomics-aided diagnosis of PDAC based on plain CT images. Its texture features can potentially enhance radiologists' imaging interpretation abilities.

Radiomics of rectal cancer for predicting distant metastasis and overall survival.

BACKGROUND: Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients. AIM: To build a novel model for predicting the presence of distant metastases and 3-year overall survival (OS) in RC patients. METHODS: This was a retrospective analysis of 148 patients (76 males and 72 females) with RC treated with curative resection, without neoadjuvant or postoperative chemoradiotherapy, between October 2012 and December 2015. These patients were allocated to a training or validation set, with a ratio of 7:3. Radiomic features were extracted from portal venous phase computed tomography (CT) images of RC. The least absolute shrinkage and selection operator regression analysis was used for feature selection. Multivariate logistic regression analysis was used to develop the radiomics signature (Rad-score) and the clinicoradiologic risk model (the combined model). Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC. The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis. RESULTS: A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score (consisted of three selected features) and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21 vs 0.32 ± 0.24 for the Rad-score, and 0.60 ± 0.23 vs 0.28 ± 0.26 for the combined model; P < 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological N-stage, and T-stage. The addition of histologic grade to the model failed to show incremental prognostic value. The combined model showed good discrimination, with areas under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups. CONCLUSION: This study presents a clinicoradiologic risk model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.

Maternal valproic acid exposure leads to neurogenesis defects and autism-like behaviors in non-human primates.

Despite the substantial progress made in identifying genetic defects in autism spectrum disorder (ASD), the etiology for majority of ASD individuals remains elusive. Maternal exposure to valproic acid (VPA), a commonly prescribed antiepileptic drug during pregnancy in human, has long been considered a risk factor to contribute to ASD susceptibility in offspring from epidemiological studies in humans. The similar exposures in murine models have provided tentative evidence to support the finding from human epidemiology. However, the apparent difference between rodent and human poses a significant challenge to extrapolate the findings from rodent models to humans. Here we report for the first time the neurodevelopmental and behavioral outcomes of maternal VPA exposure in non-human primates. Monkey offspring from the early maternal VPA exposure have significantly reduced NeuN-positive mature neurons in prefrontal cortex (PFC) and cerebellum and the Ki67-positive proliferating neuronal precursors in the cerebellar external granular layer, but increased GFAP-positive astrocytes in PFC. Transcriptome analyses revealed that maternal VPA exposure disrupted the expression of genes associated with neurodevelopment in embryonic brain in offspring. VPA-exposed juvenile offspring have variable presentations of impaired social interaction, pronounced stereotypies, and more attention on nonsocial stimuli by eye tracking analysis. Our findings in non-human primates provide the best evidence so far to support causal link between maternal VPA exposure and neurodevelopmental defects and ASD susceptibility in humans.