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%.     

Interleukin-11 Overexpression and M2 Macrophage Density are Associated with Angiogenic Activity in Proliferative Diabetic Retinopathy

ABSTRACT

Purpose

To investigate the expression of IL-11 and its receptor IL-11Rα and to quantify density of CD163⁺ M2 macrophages in proliferative diabetic retinopathy (PDR).     

经9次核酸检测终确诊的新型冠状病毒肺炎患者1例分析

目前新型冠状病毒肺炎(COVID-19)疫情在全球蔓延,形势严峻,并对全球公共卫生事业构成巨大挑战,快速准确的诊断COVID-19对疫情的防控有重要意义。本文报道1例COVID-19患者的调查和确诊过程,以对COVID-19确诊和疫情防控提供有价值的参考。患者为赴渝务工于2020年1月23日返乡人员,在渝期间,其同工地的一同事确诊为COVID-19,之后该工地接连有另外8位同事被确诊。患者返乡后2月2日被当地疫情防控单位采取医学隔离。患者于隔离后不久出现咳嗽,阵发性,干咳为主,偶可咳出黄色痰液等COVID-19相关临床症状,遂被当地新型冠状病毒肺炎定点医疗单位收治,后经市级新型冠状病毒肺炎诊治专家组远程视频会诊转入四川大学华西医院资阳医院。患者先后经历9次核酸检测,在前8次核酸检测均为阴性的情况下,于第9次核酸检测结果为阳性,2月21日最终确诊为新型冠状病毒肺炎。     

Hailey–Hailey disease improved by fractional CO2 laser

Hailey–Hailey disease (HHD), also known as benign familial pemphigus, is an autosomal dominant skin condition that affects the adhesion of epidermal keratinocytes. Although the initial manifestation of flaccid vesicles on erythematous or normal skin in flexure sites frequently goes unnoticed, large, macerated, exudative plaques of superficial erosions with crusting are observed at the time of diagnosis. There is no specific treatment for HHD, and most cases are symptomatically supported. However, infrared laser ablation has been somewhat helpful. We present a case successfully treated with fractional CO₂ laser showing a long-term favourable outcome and no adverse effects. Thus, this modality could be an alternative to full ablation for this condition.     

Reading the unique DNA methylation landscape of the brain: Non-CpG methylation,hydroxymethylation, and MeCP2

DNA methylation at CpG dinucleotides is an important epigenetic regulator common to virtually all mammalian cell types, but recent evidence indicates that during early postnatal development neuronal genomes also accumulate uniquely high levels of two alternative forms of methylation, non-CpG methylation and hydroxymethylation. Here we discuss the distinct landscape of DNA methylation in neurons, how it is established, and how it might affect the binding and function of protein readers of DNA methylation. We review studies of one critical reader of DNA methylation in the brain, the Rett syndrome protein methyl CpG-binding protein 2 (MeCP2), and discuss how differential binding affinity of MeCP2 for non-CpG and hydroxymethylation may affect the function of this methyl-binding protein in the nervous system.     

Insulin-like growth factor-1 as predictive factor of difficult laryngoscopy in patients with GH-producing pituitary adenoma: A pilot study

BackgroundGrowth hormone (GH)-producing pituitary tumors account for 10 to 15% of pituitary tumors. The hypersecretion of GH may induce changes in the airway anatomy through the activation of Insulin-like Growth factor 1(IGF-1) pathway. We sought investigate the role IGF-1 as a potential predictive factor of difficult laryngoscopy in patients with GH-producing pituitary adenoma.MethodsThis study was a single center retrospective study. We included 33 patients undergoing transsphenoidal resection of GH-producing pituitary. We recorded demographic data, el-Ganzouri risk index (EGRI) and modified Look-Evaluate-Mallampati-Obstruction-Neck mobility (mLEMON) score, and pituitary hormone plasma levels. We performed ordinal logistic regression to analyze the relationship between IGF-1 and EGRI, mLEMON, and Cormack-Lehane Grade score and a multiple logistic regression to test the capability of EGRI, mLEMON and IGF-1 levels to predict Cormack-Lehane score. Receiver operating curve (ROC), area under the curve (AUC), and cut-off value of IGF-1 were calculated.ResultsOnly 14 (42.8%) and 12 (36.36%) patients showed predictive factors of difficult intubation according to EGRI and mLEMON score, respectively. IGF-1 significantly correlated with Cormack-Lehane (p = 0.005879) but not with mLEMON and EGRI (p = 0.3080 and 0.4146, respectively). In multiple regression model IGF-1 correlated only with Cormack-Lehane grade (p = 0.0089). Area under ROC was 0.8571 and cut-off value of IGF-1 was 186.15 ng/ml.ConclusionHigher IGF-1 levels correlate with the probability of having a higher Cormack-Lehane score; classical bedside scores, such as mLEMON and EGRI, were not able to predict difficult laryngoscopy in our population.     

TGFβ promotes YAP‐dependent AXL induction in mesenchymal‐type lung cancer cells

The acquisition of chemoresistance remains a major cause of cancer mortality due to the limited accessibility of targeted or immune therapies. However, given that severe alterations of molecular features during epithelial‐to‐mesenchymal transition (EMT) lead to acquired chemoresistance, emerging studies have focused on identifying targetable drivers associated with acquired chemoresistance. Particularly, AXL, a key receptor tyrosine kinase that confers resistance against targets and chemotherapeutics, is highly expressed in mesenchymal cancer cells. However, the underlying mechanism of AXL induction in mesenchymal cancer cells is poorly understood. Our study revealed that the YAP signature, which was highly enriched in mesenchymal‐type lung cancer, was closely correlated to AXL expression in 181 lung cancer cell lines. Moreover, using isogenic lung cancer cell pairs, we also found that doxorubicin treatment induced YAP nuclear translocation in mesenchymal‐type lung cancer cells to induce AXL expression. Additionally, the concurrent activation of TGFβ signaling coordinated YAP‐dependent AXL expression through SMAD4. These data suggest that crosstalk between YAP and the TGFβ/SMAD axis upon treatment with chemotherapeutics might be a promising target to improve chemosensitivity in mesenchymal‐type lung cancer.

Abbreviations

AUC

area under the curve

AXL

AXL receptor tyrosine kinase

BCL2

B‐cell lymphoma 2

CTD2

cancer target discovery and development

CTGF

connective tissue growth factor

DEG

differentially expressed genes

DOXO

doxorubicin

EMT

epithelial–mesenchymal transition

Eto

etoposide

FDA

Food and Drug Administration

ITGB3

integrin beta‐3

MAPK

mitogen‐activated protein kinase

MMP2

matrix metalloproteinase‐2

MMP9

matrix metalloproteinase‐9

mRNA

messenger RNA

NF‐κB

nuclear factor kappa‐light‐chain‐enhancer of activated B cells

SBE

SMAD binding element

SERPINE1

serpin family E member 1

siRNA

small interfering RNA

ssGSEA

single‐sample gene set enrichment analysis

TCGA

The Cancer Genome Atlas

TGFβ

transforming growth factor beta

YAP

Yes‐associated protein

YAP8SA

mutants of inhibitory phosphorylation site at eight serine to Alanine of YAP

ZEB1

zinc finger E‐box binding homeobox 1

ZEB2

zinc finger E‐box‐binding homeobox 2