Disparate macrophage responses are linked to infection outcome of Hantan virus in humans or rodents.

Hantaan virus (HTNV) is asymptomatically carried by rodents, yet causes lethal hemorrhagic fever with renal syndrome in humans, the underlying mechanisms of which remain to be elucidated. Here, we show that differential macrophage responses may determine disparate infection outcomes. In mice, late-phase inactivation of inflammatory macrophage prevents cytokine storm syndrome that usually occurs in HTNV-infected patients. This is attained by elaborate crosstalk between Notch and NF-κB pathways. Mechanistically, Notch receptors activated by HTNV enhance NF-κB signaling by recruiting IKKß and p65, promoting inflammatory macrophage polarization in both species. However, in mice rather than humans, Notch-mediated inflammation is timely restrained by a series of murine-specific long noncoding RNAs transcribed by the Notch pathway in a negative feedback manner. Among them, the lnc-ip65 detaches p65 from the Notch receptor and inhibits p65 phosphorylation, rewiring macrophages from the pro-inflammation to the pro-resolution phenotype. Genetic ablation of lnc-ip65 leads to destructive HTNV infection in mice. Thus, our findings reveal an immune-braking function of murine noncoding RNAs, offering a special therapeutic strategy for HTNV infection.

Basal cell carcinoma arising within nevus sebaceous on the right scalp in a 55-year-old male: A case report and review of literature.

We report here a case of nevus sebaceous in a 55-year-old male, who presented with a 50-year history of an asymptomatic swelling in his right scalp. The solitary, yellowish, expansile plaque over the scalp gradually became lobulated and turned dark-pigmented with spontaneous bleeding, itching discomfort, and occasional ulceration after scratching. The male's clinical presentation and histopathological findings were compatible with basal cell carcinoma arising in nevus sebaceous. At present, 5-aminolevulinic acid photodynamic therapy (ALA-PDT) emerges as a novel treatment modality which has proved safe and effective. In this case, three sessions of photodynamic therapy in combination with surgical excision were performed, leaving mild pigmentation within 3 weeks. The patient showed good cosmetic outcome, minimal scarring on the right scalp without further complications, disease recurrence or metastasis after ALA-PDT within six months.

Application of myocardial work in predicting adverse events among patients with resistant hypertension.

BACKGROUND: Hypertension is the most common chronic disease and the leading risk factor for disability and premature deaths worldwide. Approximately 10-20% of all patients with hypertension and 15-18% of the general population who are treated for hypertension have resistant hypertension (RH). Patients with RH have a higher risk of end-organ damage, such as carotid intima-media thickening, retinopathy, left ventricular hypertrophy and heart failure, myocardial infarction, stroke, impaired renal function, and death than those with controlled blood pressure. In the present study, we applied echocardiography to patients with RH to evaluate myocardial work (MW) and determine whether it is predictive for the occurrence of adverse events within 3 years. METHODS: We included 283 outpatients and inpatients aged ≥ 18 years who met the clinical criteria for RH, without arrhythmia and severe aortic valve stenosis, between July 2018 and June 2019. The patients were followed up for 3 years from starting enrollment, and any adverse event that occurred during the period was used as the observation end point. Each enrolled patient underwent a complete transthoracic echocardiogram examination, blood pressure was measured and recorded, and MW was then analyzed. RESULTS: Eighty-two (28.98%) patients with RH had adverse events, such as myocardial infarction (n = 29, 35.36%), heart failure (n = 4, 0.05%), renal insufficiency (n = 40, 48.78%), renal failure (n = 2, 0.02%), cerebral infarction (n = 5, 0.06%), and cerebral hemorrhage (n = 2, 0.02%), and no death events occurred. In patients with RH and adverse events, global longitudinal strain (GLS) (- 16% vs. - 18%), the global work index (2079 mmHg% vs. 2327 mmHg%), global constructive work (2321 mmHg% vs. 2610 mmHg%), and global work efficiency (93% vs. 94%) were lower than those in patients without adverse events. However, global wasted work (GWW) was higher in patients with RH and adverse events than in those without adverse events (161 mmHg% vs. 127 mmHg%). GLS and GWW were the most significant in predicting adverse events. CONCLUSIONS: MW, especially GLS and GWW, is a good method to predict 3-year adverse events in patients with RH.

Xylomolins O-X: New limonoids from the Thai mangrove Xylocarpus moluccensis.

Ten new limonoids, named xylomolins O-X, were isolated from seeds of the mangrove Xylocarpus moluccensis, collected in the mangrove swamp of Trang Province, Thailand. Their structures were elucidated on the basis of comprehensive spectroscopic data analysis. The absolute configurations of five compounds (1, 3, 8-10) were unequivocally determined by single-crystal X-ray diffraction analyses, conducted with Cu Kα radiation. Xylomolins OU (1-7) are structurally intriguing mexicanolides, and xylomolin V (8) is a derivative of azadirone. Xylomolin W (9) is the first phragmalin 1,8,9-orthoester with report on X-ray crystallography from the genus Xylocarpus. In addition, xylomolin X (10) is the fifth member of the khayalactone class of limonoids with a hexahydro-2H-2,5-propanocyclopenta[b]furan motif. Compounds 1-10 inhibited NO production in LPS-activated RAW 264.7 macrophages in the range of 10.45-95.47% at the concentration of 100.0 µM. Xylomolin X (10) and xylomolin V (8), exhibited the most potent activity with IC50 values of 9.90 ± 1.84 µM and 14.66 ± 2.33 µM, respectively.

Globular adiponectin-mediated vascular remodeling by affecting the secretion of adventitial-derived tumor necrosis factor-α induced by urotensin II.

OBJECTIVES: In this study, we explored how adiponectin mediated urotensin II (UII)|-induced tumor necrosis factor-|α (TNF-|α) and α|-smooth muscle actin (α|-SMA) expression and ensuing intracellular signaling pathways in adventitial fibroblasts (AFs). METHODS: Growth-arrested AFs and rat tunica adventitia of vessels were incubated with UII and inhibitors of signal transduction pathways for 1|‒|24 h. The cells were then harvested for TNF-α receptor (TNF-|α-R) messenger RNA (mRNA) and TNF-|α protein expression determination by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. Adiponectin and adiponectin receptor (adipoR) expression was measured by RT-PCR, quantitative real-time PCR (qPCR), immunohistochemical analysis, and cell counting kit-8 (CCK-8) cell proliferation experiments. We then quantified TNF-α and α-SMA mRNA and protein expression levels by qPCR and immunofluorescence (IF) staining. RNA interference (RNAi) was used to explore the function of the adipoR genes. To investigate the signaling pathway, we applied western blotting (WB) to examine phosphorylation of adenosine 5'-monophosphate (AMP)|-activated protein kinase (AMPK). In vivo, an adiponectin (APN)|-knockout (APN-KO) mouse model mimicking adventitial inflammation was generated to measure TNF-α and α|-SMA expression by application of qPCR and IF, with the goal of gaining a comprehensive atlas of adiponectin in vascular remodeling. RESULTS: In both cells and tissues, UII promoted TNF-α protein and TNF-α-R secretion in a dose- and time-dependent manner via Rho/protein kinase C (PKC) pathway. We detected marked expression of adipoR1, T-cadherin, and calreticulin as well as a moderate presence of adipoR2 in AFs, while no adiponectin was observed. Globular adiponectin (gAd) fostered the growth of AFs, and acted in concert with UII to induce α-SMA and TNF-α through the adipoR1/T-cadherin/calreticulin/AMPK pathway. In AFs, gAd and UII synergistically induced AMPK phosphorylation. In the adventitial inflammation model, APN deficiency up-regulated the expression of α-SMA, UII receptor (UT), and UII while inhibiting TNF-|α expression. CONCLUSIONS: From the results of our study, we can speculate that UII induces TNF|-|α protein and TNF-|α|-R secretion in AFs and rat tunica adventitia of vessels via the Rho and PKC signal transduction pathways. Thus, it is plausible that adiponectin is a major player in adventitial progression and could serve as a novel therapeutic target for cardiovascular disease administration.

Prognostic analysis of pT1-T2aN0M0 cervical adenocarcinoma based on random survival forest analysis and the generation of a predictive nomogram.

Background: The efficacy of adjuvant radiotherapy for postoperative patients with early-stage cervical adenocarcinoma who are lymph node-negative is still inconclusive. Establishing a nomogram to predict the prognosis of such patients could facilitate clinical decision-making. Methods: We recruited 4636 eligible patients with pT1-T2aN0M0 cervical adenocarcinoma between 2004 and 2016 from the Surveillance, Epidemiology and End Results (SEER) database. Random survival forest (RSF) and conditional survival forest (CSF) model was used to assess the prognostic importance of each clinical characteristic variable. We identified independent prognostic factors associated with overall survival (OS) by univariate and multivariate Cox regression risk methods and then constructed a nomogram. We stratified patients based on nomogram risk scores and evaluated the survival benefit of different adjuvant therapies. To reduce confounding bias, we also used propensity score matching (PSM) to match the cohorts before performing survival analyses. Results: The RSF and CSF model identified several important variables that are associated with prognosis, including grade, age, radiotherapy and tumor size. Patients were randomly divided into training and validation groups at a ratio of 7:3. Multivariate cox analysis revealed that age, grade, tumor size, race, radiotherapy and histology were independent prognostic factors for overall survival. Using these variables, we then constructed a predictive nomogram. The C-index value for evaluating the prognostic nomogram fluctuated between 0.75 and 0.91. Patients were divided into three subgroups based on risk scores, and Kaplan-Meier (K-M) survival analysis revealed that in the low-risk group, postoperative chemotherapy alone was associated with a significantly worse OS than surgery alone. Following PSM, survival analysis showed that compared with surgery alone, radiotherapy was associated with a worse OS in the training group although there was no significant difference in the validation group. Conclusions: For patients with pT1-T2aN0M0 cervical adenocarcinoma, adjuvant treatments such as postoperative radiotherapy or chemotherapy, compared with surgery alone, are of no benefit with regards to patient survival. Our prognostic nomogram exhibits high accuracy for predicting the survival of patients with early-stage postoperative cervical adenocarcinoma.

Frailty index and risk of cardiovascular diseases: a mendelian randomization study.

Background: Previous epidemiological evidence has suggested that frailty status might be associated with cardiovascular diseases (CVDs). However, the exact causality remains unestablished. In this study, we employed Mendelian randomization and sought to investigate the potential causality in association of frailty index (FI) with cardiovascular outcomes [coronary artery disease (CAD), myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF)]. Methods: Independent single nucleotide polymorphisms (SNPs) at genome-wide significance for FI were obtained from a recent genome-wide association study (GWAS) meta-analysis of European descent (n=175,226). The association of these SNPs with CVDs was examined in summary statistics from corresponding GWASs of European descent (CAD: 184,305 cases and 60,801 controls; MI: 184,305 cases and 43,676 controls; AF: 1,030,836 cases and 60,620 controls; and HF: 977,323 cases and 47,309 controls). Replication analyses were performed using GWAS datasets from FinnGen. Results: In the meta-analysis of inverse-variance weighted estimates from different data sources, genetically determined higher FI conferred an odds ratio (OR) of 1.46 [95% confidence interval (CI): 1.13 to 1.87; P=0.003] for CAD, 1.62 (95% CI: 1.21 to 2.17, P=0.001) for MI, and 1.46 (95% CI: 1.24 to 1.72; P=4.89×10-6) for HF. However, FI failed to be potentially influential on AF risk (OR, 1.43; 95% CI: 0.93 to 1.66; P=0.107). Several complementary analyses also received broadly concordant results. Conclusions: We have provided genetic evidence of a causal association between FI and the risk of CAD, MI, and HF. Further studies are warranted to clarify whether FI is causally related to AF risk.

ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps.

BACKGROUND: Acute ischemic stroke is one of the leading death causes. Delineating stoke infarct core in medical images plays a critical role in optimal stroke treatment selection. However, accurate estimation of infarct core still remains challenging because of 1) the large shape and location variation of infarct cores; 2) the complex relationships between perfusion parameters and final tissue outcome. METHODS: We develop an encoder-decoder based semantic model, i.e., Ischemic Stroke Prediction Network (ISP-Net), to predict infarct core after thrombolysis treatment on CT perfusion (CTP) maps. Features of native CTP, CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume), MTT (Mean Transit Time), Tmax are generated and fused with five-path convolutions for comprehensive analysis. A multi-scale atrous convolution (MSAC) block is firstly put forward as the enriched high-level feature extractor in ISP-Net to improve prediction accuracy. A retrospective dataset which is collected from multiple stroke centers is used to evaluate the performance of ISP-Net. The gold standard infarct cores are delineated on the follow-up scans, i.e., non-contrast CT (NCCT) or MRI diffusion-weighted image (DWI). RESULTS: In clinical dataset cross-validation, we achieve mean Dice Similarity Coefficient (DSC) of 0.801, precision of 81.3%, sensitivity of 79.5%, specificity of 99.5%, Area Under Curve (AUC) of 0.721. Our approach yields better outcomes than several advanced deep learning methods, i.e., Deeplab V3, U-Net++, CE-Net, X-Net and Non-local U-Net, demonstrating the promising performance in infarct core prediction. No significant difference of the prediction error is shown for the patients with follow-up NCCT and follow-up DWI (P >0.05). CONCLUSION: This study provides an approach for fast and accurate stroke infarct core estimation. We anticipate the prediction results of ISP-Net could offer assistance to the physicians in the thrombolysis or thrombectomy therapy selection.

Cardiac angiosarcoma: A case report and review of the literature.

Primary cardiac tumors are extremely rare, among which malignancies comprise about 15-25%. As the most common type of primary cardiac malignancies, angiosarcomas tend to arise in the right heart, especially right atrium. In this case report, we presented a 32-year-old female with primary cardiac angiosarcoma in the right atrial appendage detected by transesophageal echocardiography, as it is difficult to display on conventional transthoracic echocardiography.

Dissected aorta segmentation using convolutional neural networks.

BACKGROUND AND OBJECTIVE: Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed. METHOD: In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area. RESULTS: The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%. CONCLUSIONS: The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps.

mGluR5-Mediated eCB Signaling in the Nucleus Accumbens Controls Vulnerability to Depressive-Like Behaviors and Pain After Chronic Social Defeat Stress.

Stress contributes to major depressive disorder (MDD) and chronic pain, which affect a significant portion of the global population, but researchers have not clearly determined how these conditions are initiated or amplified by stress. The chronic social defeat stress (CSDS) model is a mouse model of psychosocial stress that exhibits depressive-like behavior and chronic pain. We hypothesized that metabotropic glutamate receptor 5 (mGluR5) expressed in the nucleus accumbens (NAc) normalizes the depressive-like behaviors and pain following CSDS. Here, we show that CSDS induced both pain and social avoidance and that the level of mGluR5 decreased in susceptible mice. Overexpression of mGluR5 in the NAc shell and core prevented the development of depressive-like behaviors and pain in susceptible mice, respectively. Conversely, depression-like behaviors and pain were exacerbated in mice with mGluR5 knockdown in the NAc shell and core, respectively, compared to control mice subjected to 3 days of social defeat stress. Furthermore, (RS)-2-chloro-5-hydroxyphenylglycine (CHPG), an mGluR5 agonist, reversed the reduction in the level of the endocannabinoid (eCB) 2-arachidonoylglycerol (2-AG) in the NAc of susceptible mice, an effect that was blocked by 3-((2-methyl-1, 3-thiazol-4-yl) ethynyl) pyridine hydrochloride (MTEP), an mGluR5 antagonist. In addition, the injection of CHPG into the NAc shell and core normalized depressive-like behaviors and pain, respectively, and these effects were inhibited by AM251, a cannabinoid type 1 receptor (CB1R) antagonist. Based on these results, mGluR5-mediated eCB production in the NAc relieves stress-induced depressive-like behaviors and pain.

An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging.

Current thrombolysis for acute ischemic stroke (AIS) treatment strictly relies on the time since stroke (TSS) less than 4.5 h. However, some patients are excluded from thrombolytic treatment because of the unknown TSS. The diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch can simply identify TSS since lesion intensities are not identical at different onset time. In this paper, we propose an automatic machine learning method to classify the TSS less than or more than 4.5 h. First, we develop a cross-modal convolutional neural network to accurately segment the stroke lesions from DWI and FLAIR images. Second, the features are extracted from DWI and FLAIR according to the segmentation regions of interest (ROI). Finally, the features are fed to machine learning models to identify TSS. In DWI and FLAIR ROI segmentation, the networks obtain high Dice coefficients with 0.803 and 0.647. The classification test results show that our model achieves an accuracy of 0.805, with a sensitivity of 0.769 and a specificity of 0.840. Our approach outperforms human reading DWI-FLAIR mismatch model, illustrating the potential for automatic and fast TSS identification.

Adiponectin, but Not TGF-ß1, CTGF, IL-6 or TNF-α, May Be a Potential Anti-Inflammation and Anti-Fibrosis Factor in Keloid.

INTRODUCTION: Numerous studies have elucidated adiponectin as a negative impact on inflammation and tissue fibrosis. However, little is known about the relevance between adiponectin and inflammatory factors in keloid. METHODS: To clarify whether adiponectin plays a role in the inflammation and fibrosis of keloid, 50 patients with keloid and 50 healthy subjects were enrolled, We examined the serum and mRNA expression levels of adiponectin, TGF-ß1, CTGF, IL-6 and TNF-α in normal skin tissues and keloid tissues by ELISA and qPCR, respectively. Correlation analysis between serum concentration of adiponectin with Vancouver Scar Scale (VSS) scores and the age of patients with keloid was evaluated, and the adiponectin concentrations in patients with keloid between different genders were measured. We further examined the effects of adiponectin on TGF-ß1 mediated expression of collagen I, FN and MMP-1 in normal fibroblasts (NFs) and keloid fibroblasts (KFs). RESULTS: We discovered that lower serum concentration and mRNA expression of adiponectin, but higher TGF-ß1, CTGF, IL-6 and TNF-α levels were measured in patients with keloid compared with those in normal controls. Furthermore, there was a strong inverse correlation between the serum adiponectin levels and VSS scores in patients with keloid, but not in ages, and there was no statistically difference between different genders. Moreover, adiponectin attenuated TGF-ß1 mediated expression of collagen I and FN, and upregulated the expression level of MMP-1 in KFs, but not in NFs. In addition, the inhibitory effect of adiponectin on TGF-ß1 was attenuated by AMPK inhibitor Compound C, but not PI3K/Akt inhibitor LY294002. DISCUSSION: Adiponectin may exert an anti-inflammation and anti-fibrosis role in the development of keloid. One of the underlying mechanisms may be the activation of the AMPK signaling pathway.

Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network.

Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.

Microglial TLR4-induced TAK1 phosphorylation and NLRP3 activation mediates neuroinflammation and contributes to chronic morphine-induced antinociceptive tolerance.

BACKGROUND AND PURPOSE: The aim of this work was to investigate the role and signal transduction of toll-like receptor 4 (TLR4), TGF-ß-activated kinase 1 (TAK1) and nod-like receptor protein 3 (NLRP3) in microglial in the development of morphine-induced antinociceptive tolerance. METHODS: TLR4 and NLRP3 knockout mice and 5Z-7-oxozeaeno (a selective inhibitor against TAK1 activity) were used to observe their effect on the development of morphine tolerance. Intrathecal injections of morphine (0.75 mg/kg once daily for 7 days) were used to establish anti-nociceptive tolerance, which was measured by the tail-flick test. Spinal TLR4, TAK1, and NLRP3 expression levels and phosphorylation of TAK1 were evaluated by Western blotting and immunofluorescence. RESULTS: Repeated treatment with morphine increased total expression of spinal TLR4, TAK1, and NLRP3 and phosphorylation of TAK1 in wild-type mice. TLR4 knockout attenuated morphine-induced tolerance and inhibited the chronic morphine-induced increase in NLRP3 and phosphorylation of TAK1. Compared with controls, mice that received 5Z-7-oxozeaenol showed decreased development of morphine tolerance and inhibition on repeated morphine-induced increase of NLRP3 but not TLR4. NLRP3 knockout mice showed resistance to morphine-induced analgesic tolerance with no effect on chronic morphine-induced expression of TLR4 and TAK1. TLR4, TAK1, and NLRP3 were collectively co-localized together and with the microglia marker Iba1. CONCLUSIONS: Microglial TLR4 regulates TAK1 expression and phosphorylation and results in NLRP3 activation contributes to the development of morphine tolerance through regulating neuroinflammation. Targeting TLR4-TAK1-NLRP3 signaling to regulate neuro-inflammation will be alternative therapeutics and strategies for chronic morphine-induced antinociceptive tolerance.

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification and lesion-specific segmentation network is proposed for automatic esophageal lesion annotation at pixel level. For the established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718, and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655, and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate, and reliable esophageal lesion diagnosis in clinics.

The Current State of Research Regarding the Role of Non-Coding RNAs in Cutaneous Squamous Cell Carcinoma.

Skin cancers, including those of both both melanoma and non-melanoma subtypes, remain among the most common forms of human cancer. Non-melanoma skin cancers are typically further differentiated into the basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC) categories. Current approaches to diagnosing and treating cSCC remain unsatisfactory, and the prognosis for patients with this disease is relatively poor. Recent advances in high-throughput sequencing have led to an increasingly robust understanding of the diversity of non-coding RNAs (ncRNAs) expressed in both physiological and pathological contexts. These ncRNAs include microRNAs, long ncRNAs, and circular RNAs, all of which have been found to play key functional roles and/or to have value as diagnostic biomarkers or therapeutic targets in a range of different disease contexts. The number of ncRNAs associated with cSCC continues to rise, and as such, there is clear value in comprehensively reviewing the functional roles of these molecules in this form of cancer in order to highlight future avenues for research and clinical development.

Persistent Rheb-induced mTORC1 activation in spinal cord neurons induces hypersensitivity in neuropathic pain.

The small GTPase Ras homolog enriched in the brain (Rheb) can activate mammalian target of rapamycin (mTOR) and regulate the growth and cell cycle progression. We investigated the role of Rheb-mediated mTORC1 signaling in neuropathic pain. A chronic constriction injury (CCI) model was dopted. CCI induced obvious spinal Rheb expression and phosphorylation of mTOR, S6, and 4-E-BP1. Blocking mTORC1 signal with rapamycin alleviated the neuropathic pain and restored morphine efficacy in CCI model. Immunofluoresence showed a neuronal co-localization of CCI-induced Rheb and pS6. Rheb knockin mouse showed a similar behavioral phenotype as CCI. In spinal slice recording, CCI increased the firing frequency of neurons expressing HCN channels; inhibition of mTORC1 with rapamycin could reverse the increased spinal neuronal activity in neuropathic pain. Spinal Rheb is induced in neuropathic pain, which in turn active the mTORC1 signaling in CCI. Spinal Rheb-mTOR signal plays an important role in regulation of spinal sensitization in neuropathic pain, and targeting mTOR may give a new strategy for pain management.

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.

Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet outperforms some state-of-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.

MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection.

Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimensional nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimensional CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimensional CNNs (2D CNNs) with attention module. To incorporate different sizes and shapes of nodule candidates, a multi-scale ensemble strategy is employed for probability aggregation with weights. The proposed method is evaluated on the LUNA16 challenge dataset in ISBI 2016 with ten-fold cross-validation. Experiment results show that the proposed framework achieves classification performance with a CPM score of 0.9008. All of these indicate that our method enables an efficient, accurate and reliable pulmonary nodule detection for clinical diagnosis.