Connexin 36 Mediates Orofacial Pain Hypersensitivity Through GluK2 and TRPA1

Trigeminal neuralgia is a debilitating condition, and the pain easily spreads to other parts of the face. Here, we established a mouse model of partial transection of the infraorbital nerve (pT-ION) and found that the Connexin 36 (Cx36) inhibitor mefloquine caused greater alleviation of pT-ION-induced cold allodynia compared to the reduction of mechanical allodynia. Mefloquine reversed the pT-ION-induced upregulation of Cx36, glutamate receptor ionotropic kainate 2 (GluK2), transient receptor potential ankyrin 1 (TRPA1), and phosphorylated extracellular signal regulated kinase (p-ERK) in the trigeminal ganglion. Cold allodynia but not mechanical allodynia induced by pT-ION or by virus-mediated overexpression of Cx36 in the trigeminal ganglion was reversed by the GluK2 antagonist NS102, and knocking down Cx36 expression in Nav1.8-expressing nociceptors by injecting virus into the orofacial skin area of Nav1.8-Cre mice attenuated cold allodynia but not mechanical allodynia. In conclusion, we show that Cx36 contributes greatly to the development of orofacial pain hypersensitivity through GluK2, TRPA1, and p-ERK signaling.Electronic supplementary materialThe online version of this article (10.1007/s12264-020-00594-4) contains supplementary material, which is available to authorized users.     

Can EGFR mutation status be reliably determined in pre‐operative needle biopsies from adenocarcinomas of the lung?

The identification of EGFR mutations in non‐small‐cell lung cancer is important for selecting patients, who may benefit from treatment with EGFR tyrosine kinase inhibitors. The analysis is usually performed on cytological aspirates and/or histological needle biopsies, representing a small fraction of the tumour volume. The aim of the present investigation was to evaluate the diagnostic performance of this molecular test. We retrospectively included 201 patients with primary adenocarcinoma of the lung. EGFR mutation status (exon 19 deletions and exon 21 L858R point mutation) was evaluated on both pre‐operative biopsies (131 histological and 70 cytological) and on the surgical specimens, using PCR. Samples with low tumour cell fraction were assigned to laser micro‐dissection (LMD). We found nine (4.5%) patients with EGFR mutation in the lung tumour resections, but failed to identify mutation in one of the corresponding pre‐operative, cytological specimens. Several (18.4%) analyses of the pre‐operative biopsies were inconclusive, especially in case of biopsies undergoing LMD and regarding exon 21 analysis. Discrepancy of mutation status in one patient may reflect intra‐tumoural heterogeneity or technical issues. Moreover, several inconclusive results in the diagnostic biopsies reveal that attention must be paid on the suitability of pre‐operative biopsies for EGFR mutation analysis.     

First Japanese autopsy case showing LRRK2 mutation G2019S and TDP-43 proteinopathy

This is the first Japanese autopsy case of Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation with atypical TDP43 proteinopathy. Our case is important that presented clinically dysphagia and pathologically TDP-43 proteinopathy. TDP43 may play an important role of clinical presentation with LRRK2 G2019S mutation carriers.     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.