Exploring and Exploiting Multi-modality Uncertainty for Tumor Segmentation on PET/CT.

Despite the success of deep learning methods in multi-modality segmentation tasks, they typically produce a deterministic output, neglecting the underlying uncertainty. The absence of uncertainty could lead to over-confident predictions with catastrophic consequences, particularly in safety-critical clinical applications. Recently, uncertainty estimation has attracted increasing attention, offering a measure of confidence associated with machine decisions. Nonetheless, existing uncertainty estimation approaches primarily focus on single-modality networks, leaving the uncertainty of multi-modality networks a largely under-explored domain. In this study, we present the first exploration of multi-modality uncertainties in the context of tumor segmentation on PET/CT. Concretely, we assessed four well-established uncertainty estimation approaches across various dimensions, including segmentation performance, uncertainty quality, comparison to single-modality uncertainties, and correlation to the contradictory information between modalities. Through qualitative and quantitative analyses, we gained valuable insights into what benefits multi-modality uncertainties derive, what information multi-modality uncertainties capture, and how multi-modality uncertainties correlate to information from single modalities. Drawing from these insights, we introduced a novel uncertainty-driven loss, which incentivized the network to effectively utilize the complementary information between modalities. The proposed approach outperformed the backbone network by 4.53 and 2.92 Dices in percentages on two PET/CT datasets while achieving lower uncertainties. This study not only advanced the comprehension of multi-modality uncertainties but also revealed the potential benefit of incorporating them into the segmentation network. The code is available at https://github.com/HUST-Tan/MMUE.

An Exploration of Osteosarcoma Metastasis Diagnostic Markers Based on Tumor-Associated Neutrophils.

BACKGROUND: The high rate of the recurrence and metastasis of osteosarcoma (OS) is the major cause of its poor prognosis. There is a strong correlation between tumor-associated neutrophils (TANs) and tumor progression, progression, and metastasis. This study aimed to identify potential markers that could predict OS metastasis based on analysis of TANs in the tissues of OS patients. METHODS: A single-cell sequencing dataset (GSE152048), containing seven primary OS lesions, two recurrent OS lesions, and two lung metastatic OS lesions was used for TANs subset identification using the R software (version 4.1.0, R Project for Statistical Computing, Vienna, Austria; https://www.r-project.org/). Immune cell infiltration and immune score were analyzed using CIBERSORT algorithm and ESTIMATE database, respectively. The differentially expressed genes (DEGs) of TANs were used for weighted gene co-expression network analysis (WGCNA) to screen key genes associated with OS metastasis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were used to analyze the functions and signaling pathways involved in key genes. The mRNA levels of protein phosphatase 2 regulatory subunit B'gamma (PPP2R5C) were validated in human osteosarcoma cell lines U2-OS and MG63, human normal cervical endometrial cell line HUCEC, and human foreskin fibroblast (HFF-1) cell line by real-time qPCR (RT-qPCR). PPP2R5C-siRNA991 was transfected into U2-OS and MG63 for 48 h, then the expression levels of PPP2R5C, AKT serine/threonine kinase (AKT), and phospho-AKT (p-AKT) were determined by RT-qPCR and Western blotting. Cell proliferation, migration, and apoptosis were measured by cell counting kit-8 (CCK-8), Transwell, and flow cytometry, respectively. RESULTS: We identified TANs subsets in primary, metastatic, and recurrent OS. Immune infiltration analysis showed that TANs were expressed in OS. Compared with non-metastatic OS, metastatic OS had lower stromal score, immune score, ESTIMATE score, and higher tumor purity. WGCNA classified DEGs into five clusters, according to their function and identified PPP2R5C, protein phosphatase 2 regulatory subunit B'epsilon (PPP2R5E), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma (YWHAG) and CREB binding protein (CREBBP), as potential markers that may affect TANs-induced OS metastasis via hypoxia inducible factor 1 (HIF-1), phosphatidylinositol 3-kinases (PI3K)-AKT and Janus kinase (JAK)-signal transducers and activators of transcription (STAT) signaling pathways. In vitro experiments demonstrated that the mRNA and protein expressions of PPP2R5C, PPP2R5E, YWHAG, and CREBBP were highly expressed in U2-OS and MG63 cells (p < 0.01). Furthermore, PPP2R5C reduced proliferation and migration (p < 0.01) and increased apoptosis and p-AKT protein levels in U2-OS and MG6 cells (p < 0.01). CONCLUSIONS: PPP2R5C affects OS metastasis via PI3K/AKT pathway, which may be a potential marker for OS metastasis and recurrence.

The ferroptosis signature predicts the prognosis and immune microenvironment of nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is a cancer with a high metastatic rate and poor prognosis. Growing studies suggest that ferroptosis take part in the development of tumours. At the same time, the connection between ferroptosis-related genes (FRGs) and the prognosis of NPC remains unclear. In this study, we explored the dysregulated FRGs between normal control and tumour samples of NPC. Firstly, 14 of 36 differentially expressed FRGs were identified in NPC tissues compared to normal tissues, among which ABCC1, GLS2, CS and HMGCR were associated with poor prognosis for patients. The four ferroptosis genes were used for consensus cluster analysis and two risk-related FRGs (ABCC1 and GLS2) were used in a risk model. The ROC curve revealed the good predictive performance of this risk signature. Multivariate analysis revealed that risk score and intratumoral TILs were independent risk factors linked to prognosis. Additionally, our results suggested that the risk signature was attached to the immune microenvironment. Moreover, the NPC patients with high risk were sensitive to chemotherapeutic drugs including axitinib, docetaxel, embelin, epothilone.B, parthenolide, thapsigargin, tipifarnib, vinorelbine. Finally, the expression of ABCC1 and GLS2 was validated in NPC tissues using immunohistochemistry. Together, these results revealed ferroptosis may be a potential biomarker in NPC and representing a promising future direction in prognosis and therapeutic strategy for the treatment of NPC.

Integrative Analysis Identified CD38 As a Key Node That Correlates Highly with Immunophenotype, Chemoradiotherapy Resistance, And Prognosis of Head and Neck Cancer.

Head and neck cancer (HNC) is mainly treated by surgery, radiotherapy, and adjuvant chemotherapy; however, the prognosis of some patients with HNC is poor because of radiotherapy and chemotherapy resistance. In recent years, anti­PD­1 monoclonal antibodies have shown certain efficacy, and a change of the tumor immune microenvironment is the main reason for the failure of HNC immunotherapy. The present study aimed to identify and verify that CD38, which is closely related to the prognosis of HNC, is a potential biological marker of radiotherapy and chemotherapy resistance and PD-L1 immunotherapy resistance via a comprehensive bioinformatic analysis in The Cancer Genome Atlas and Gene Expression Omnibus databases. According to the UALCAN database, the transcript level of CD38 in HNC was analyzed using cluster analysis, and the expression of CD38 mRNA in HNC was detected using the Oncomine database. The characteristics of CD38-related oncogenes were identified by gene cluster enrichment analysis in LinkedOmics. The R2 and SEER databases were used to further evaluate the prognostic significance of the CD38 gene in HNC using receiver operating characteristic curve analysis of Kaplan-Meier (KM) survival and the clinical characteristics of the subjects. The protein-protein interaction network of the top 50 genes showing significant positive correlations with CD38 in HNC was analyzed using STRING. Finally, we used a nasopharyngeal carcinoma (NPC) cell line to verify the biological function. The results showed that the levels of CD38 mRNA expression in patients with HNC were significantly higher than those in healthy controls. The levels of CD38 mRNA expression in patients with HNC of different ages, sexes, and races were significantly higher than those in the healthy controls. CD38 is an independent prognostic factor for HNC, and high expression of CD38 indicates poor prognosis. CD38 expression correlated positively with the markers of many kinds of immune cells, and correlated significantly with the expression of PD-L1. We found that the high expression of CD38 suggested a poor prognosis in the subgroup of tumors treated with chemotherapeutic drugs in the G1/S phase. We used HNC cell lines to verify that the high expression of CD38 promoted the proliferation of NPC cells and produced radiotherapy tolerance. Through comprehensive bioinformatics analysis, we suggested that CD38 is a key gene involved in radiotherapy, chemotherapy, and immune drug resistance in HNC. This study provides a reliable biomarker to predict the prognosis of patients with HNC and a reference for clinical comprehensive treatment of HNC. Individualization combined with CD38 monoclonal antibodies might provide a promising treatment strategy for this fatal disease, and this comprehensive treatment might reduce the damage to normal tissue and improve the prognosis and quality of life of patients with HNC.

Rapid determination of 14 odorous compounds in drinking water using gas chromatography-mass spectrometry coupled with headspace solid-phase microextraction pretreatment.

A sensitive and effective method was developed for the simultaneous determination of 14 odorous compounds in drinking water using gas chromatography-mass spectrometry (GC-MS) coupled with headspace solid phase microextraction (HS-SPME) pretreatment. The influencing factors including SPME fiber, ionic strength, temperature and time on the pretreatment procedure were evaluated systematically. Under the optimized conditions, 10 mL of the samples added with 1.0 g sodium chloride was extracted by CAR/PDMS fiber at 60 °C for 30 min and then desorbed at 280 °C for 3 min. The analytes achieved good linearity as the mass concentrations were in the range of 0.0020-10.0 µg L-1 with correlation coefficients higher than 0.9990. The limits of detection (LODs) ranging between 0.2 and 50 ng L-1 were lower than the olfactory threshold of these studied compounds. Satisfactory recoveries were obtained ranging from 84.8% to 110.6%, and good reproducibility indicated by relative standard deviation (RSD) in the range of 0.50-9.5% was obtained as well. The proposed method was convenient, sensitive and accurate, which was suitable for the routine monitoring 14 odorous compounds in water.

Genome-Wide Identification and Evolution Analysis of R2R3-MYB Gene Family Reveals S6 Subfamily R2R3-MYB Transcription Factors Involved in Anthocyanin Biosynthesis in Carrot.

The taproot of purple carrot accumulated rich anthocyanin, but non-purple carrot did not. MYB transcription factors (TFs) condition anthocyanin biosynthesis in many plants. Currently, genome-wide identification and evolution analysis of R2R3-MYB gene family and their roles involved in conditioning anthocyanin biosynthesis in carrot is still limited. In this study, a total of 146 carrot R2R3-MYB TFs were identified based on the carrot transcriptome and genome database and were classified into 19 subfamilies on the basis of R2R3-MYB domain. These R2R3-MYB genes were unevenly distributed among nine chromosomes, and Ka/Ks analysis suggested that they evolved under a purified selection. The anthocyanin-related S6 subfamily, which contains 7 MYB TFs, was isolated from R2R3-MYB TFs. The anthocyanin content of rhizodermis, cortex, and secondary phloem in 'Black nebula' cultivar reached the highest among the 3 solid purple carrot cultivars at 110 days after sowing, which was approximately 4.20- and 3.72-fold higher than that in the 'Deep purple' and 'Ziwei' cultivars, respectively. The expression level of 7 MYB genes in purple carrot was higher than that in non-purple carrot. Among them, DcMYB113 (DCAR_008994) was specifically expressed in rhizodermis, cortex, and secondary phloem tissues of 'Purple haze' cultivar, with the highest expression level of 10,223.77 compared with the control 'DPP' cultivar at 70 days after sowing. DcMYB7 (DCAR_010745) was detected in purple root tissue of 'DPP' cultivar and its expression level in rhizodermis, cortex, and secondary phloem was 3.23-fold higher than that of secondary xylem at 110 days after sowing. Our results should be useful for determining the precise role of S6 subfamily R2R3-MYB TFs participating in anthocyanin biosynthesis in carrot.

Bridging Feature Gaps to Improve Multi-Organ Segmentation on Abdominal Magnetic Resonance Image.

Accurate segmentation of abdominal organs on MRI is crucial for computer-aided surgery and computer-aided diagnosis. Most state-of-the-art methods for MRI segmentation employ an encoder-decoder structure, with skip connections concatenating shallow features from the encoder and deep features from the decoder. In this work, we noticed that simply concatenating shallow and deep features was insufficient for segmentation due to the feature gap between shallow features and deep features. To mitigate this problem, we quantified the feature gap from spatial and semantic aspects and proposed a spatial loss and a semantic loss to bridge the feature gap. The spatial loss enhanced spatial details in deep features, and the semantic loss introduced semantic information into shallow features. The proposed method successfully aggregated the complementary information between shallow and deep features by formulating and bridging the feature gap. Experiments on two abdominal MRI datasets demonstrated the effectiveness of the proposed method, which improved the segmentation performance over a baseline with nearly zero additional parameters. Particularly, the proposed method has advantages for segmenting organs with blurred boundaries or in a small scale, achieving superior performance than state-of-the-art methods.

Endoplasmic reticulum stress and protein degradation in chronic liver disease.

Endoplasmic reticulum (ER) stress is easily observed in chronic liver disease, which often causes accumulation of unfolded or misfolded proteins in the ER, leading to unfolded protein response (UPR). Regulating protein degradation is an integral part of UPR to relieve ER stress. The major protein degradation system includes the ubiquitin-proteasome system (UPS) and autophagy. All three arms of UPR triggered in response to ER stress can regulate UPS and autophagy. Accumulated misfolded proteins could activate these arms, and then generate various transcription factors to regulate the expression of UPS-related and autophagy-related genes. The protein degradation process regulated by UPR has great significance in many chronic liver diseases, including non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), viral hepatitis, liver fibrosis, and hepatocellular carcinoma(HCC). In most instances, the degradation of excessive proteins protects cells with ER stress survival from apoptosis. According to the specific functions of protein degradation in chronic liver disease, choosing to promote or inhibit this process is promising as a potential method for treating chronic liver disease.

Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.

PET and CT are widely used imaging modalities in radiation oncology. PET imaging has a high contrast but blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between tumor and soft normal tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve segmentation accuracy. These information can be either consistent or inconsistent in the image-level. How to correctly localize tumor edges with these inconsistent information is a major challenge for co-segmentation methods. In this study, we proposed a novel variational method for tumor co-segmentation in PET/CT, with a fusion strategy specifically designed to handle the information inconsistency between PET and CT in an adaptive way - the method can automatically decide which modality should be more trustful when PET and CT disagree to each other for localizing the tumor boundary. The proposed method was constructed based on the Γ-convergence approximation of the Mumford-Shah (MS) segmentation model. A PET restoration process was integrated into the co-segmentation process, which further eliminate the uncertainty for tumor segmentation introduced by the blurring of tumor edges in PET. The performance of the proposed method was validated on a test dataset with fifty non-small cell lung cancer patients. Experimental results demonstrated that the proposed method had a high accuracy for PET/CT co-segmentation and PET restoration, and can accurately estimate the blur kernel of the PET scanner as well. For those complex images in which the tumors exhibit Fluorodeoxyglucose (FDG) uptake inhomogeneity or even invade adjacent soft normal tissues, the proposed method can still accurately segment the tumors. It achieved an average dice similarity indexes (DSI) of 0.85 ± 0.06, volume error (VE) of 0.09 ± 0.08, and classification error (CE) of 0.31 ± 0.13.

Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT.

Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: 1). Only a few training samples were needed for training the designed network to produce the probability map; 2). The proposed method can be applied to small datasets, normally seen in clinic research; 3). The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); 4). The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ± 0.05, sensitivity (SE) of 0.86 ± 0.07, positive predictive value (PPV) of 0.87 ± 0.10, volume error (VE) of 0.16 ± 0.12, and classification error (CE) of 0.30 ± 0.12.

A novel mechanism by which ACTA2-AS1 promotes cervical cancer progression: acting as a ceRNA of miR-143-3p to regulate SMAD3 expression.

BACKGROUND: Long non-coding RNAs (LncRNAs) have been increasingly confirmed to be abnormally expressed in human cancer and closely related to tumorigenesis. LncRNA ACTA2-AS1 is abnormally expressed in multiple tumors and participates in their development. However, whether ACTA2-AS1 plays a role in the development of cervical cancer (CC) and the exact mechanism of its role has not been elucidated. METHODS: Quantitative real-time PCR (qRT-PCR) was conducted to detect the expression level of messenger RNA of ACTA2-AS1, miR-143-3p and SMAD3 in tumor tissues and cells. Additionally, SMAD3 protein expression by western blots in cells. Small interference RNA against ACTA2-AS1 or SMAD3 and miR-143-3p mimic/inhibitor was designed and transfected into CC cell lines to investigate their correlations and potential impacts on cell function. Cell Counting Kit-8 (CCK-8) assay, colony formation, cell cycle assay, transwell assay and flow cytometry analysis were performed to detect the specific effects on cell line proliferation, metastasis and apoptosis. RESULTS: ACTA2-AS1 was significantly increased in CC tissues and cells and miR-143-3p was down-regulated. Clinically, the higher expression of ACTA2-AS1 was significantly correlated with higher FIGO stage. Loss-of-function assay revealed that silencing of ACTA2-AS1 inhibited cell proliferation, colony formation, migration and promoted apoptosis in CC. Additionally, Pearson correlation analysis showed that the expression of ACTA2-AS1 and miR-143-3p were negatively correlated. Dual-luciferase reporter assay and further mechanistic experiments confirmed that ACTA2-AS1 could sponge and regulate the expression of miR-143-3p. Furthermore, SMAD3 was the target gene of miR-143-3p and ACTA2-AS1 could upregulate SMAD3 through acting as a competitive endogenous RNA (ceRNA) of miR-143-3p. Finally, rescue assay demonstrated that the ACTA2-AS1/miR-143-3p/SMAD3 axis played an important role in the proliferation, migration and apoptosis of CC cells. CONCLUSIONS: In summary, our study revealed that ACTA2-AS1 upregulates SMAD3 by competitively binding miR-143-3p, thereby accelerating CC progression. The ACTA2-AS1/miR-143-3p/SMAD3 axis can play a crucial role in cervical carcinogenesis, providing new clues for the early diagnosis and treatment of CC.

A novel function of IMPA2, plays a tumor-promoting role in cervical cancer.

Discovery of genes and molecular mechanism involved in cervical cancer development would promote the prevention and treatment. By comparing gene expression profiles of cervical carcinoma in situ (CCIS) and adjacent normal tissues, we identified a potential cancer-promoting gene, IMPA2. This study aimed to elucidate the role of IMPA2 and underlying molecular mechanisms in cervical cancer progression. To do this expression of IMPA2 was compared between human cervical cancer and corresponding adjacent normal cervical tissues firstly. CCK-8 assay, clone formation assay, wound healing assay, transwell assay, and tumor formation in nude mice were performed to demonstrate the effect of IMPA2 in cervical cancer proliferation and metastasis. Further proteomic profiling and western blotting explored the molecular pathway involved in the IMPA2-regulating process. The results showed that IMPA2 gene expression was upregulated in cervical cancer. Consistently, silencing of IMPA2 suppressed tumor formation in BALB/c nude mice. Short hairpin RNA (shRNA)-mediated IMPA2 silencing significantly inhibited proliferation and colony-forming abilities of cervical cancer cells, while IMPA2 overexpression had little impact. Also, IMPA2 silencing suppressed cellular migration, but overexpression promoted migration. Proteomics analysis revealed the involvement of mitogen-activated protein kinase (MAPK) pathway in tumor-promoting action of IMPA2. Significantly, the inhibition of IMPA2 activated ERK phosphorylation, and its inhibitory effects can be restored by using selective ERK inhibitor, FR180204. In conclusion, IMPA2 acts as an oncogene in the proliferation and migration of cervical cancer. IMPA2 downregulated ERK phosphorylation to promote cervical cancer. These findings identify a new mechanism underlying cervical cancer and suggest a regulating effect of IMPA2 in MAPK signaling pathway.

Long non-coding RNA LOC285194 in cancer.

Long non-coding RNAs (lncRNAs) are non-protein-encoding RNAs that are usually over 200 nucleotides-long. The development of whole-genome sequencing has enabled the identification of several lncRNAs, and the determination of their critical roles in the human tumor process. LOC285194, also known as LSAMP antisense RNA 3 and tumor suppressor candidate 7 (TUSC7), is a >2-kb-long lncRNA comprised of four exons (gene ID: 285194), and located in chr3q13.31. LOC285194 expression is reported to be consistently low in tumor cells and often associated with poor clinical outcomes. Functionally, LOC285194 overexpression has been shown to inhibit cell proliferation, invasion, and migration in vitro and in vivo. Further, LOC285194 mainly suppressed or promoted the expression of related genes through direct or indirect pathways, suggesting that LOC285194 might be a feasible biomarker or therapeutic target in human cancers. Here, we reviewed and summarized existing literature on the functions and mechanisms of LOC285194 in human cancers.

Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT.

We extracted image features from serial 18F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT), in the middle of the treatment (Mid-CRT) and post-treatment (Post-CRT) were included. For each patient, Mid-CRT and Post-CRT scans were aligned to Pre-CRT scan. Comprehensive image features were extracted from CT and PET (SUV) images within manually delineated gross tumor volume, including geometry features, intensity features and texture features. The difference of feature values between two time points were also computed and analyzed. We employed univariate logistic regression model, multivariate model, and naïve Bayesian classifier to analyze the image features and identify useful tumor recurrent predictors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the accuracy of the prediction. In univariate analysis, six geometry, three intensity, and six texture features were identified as significant predictors of tumor recurrence. A geometry feature of Roundness between Post-CRT and Pre-CRT CTs was identified as the most important predictor with an AUC value of 1.00 by multivariate logistic regression model. The difference of Number of Pixels on Border (geometry feature) between Post-CRT and Pre-CRT SUVs and Elongation (geometry feature) of Post-CRT CT were identified as the most useful feature set (AUC = 1.00) by naïve Bayesian classifier. To investigate the early prediction ability, we used features only from Pre-CRT and Mid-CRT scans. Orientation (geometry feature) of Pre-CRT SUV, Mean (intensity feature) of Pre-CRT CT, and Mean of Long Run High Gray Level Emphasis (LRHGLE) (texture feature) of Pre-CRT CT were identified as the most important feature set (AUC = 1.00) by multivariate logistic regression model. Standard deviation (intensity feature) of Mid-CRT SUV and difference of Mean of LRHGLE (texture feature) between Mid-CRT and Pre-CRT SUVs were identified as the most important feature set (AUC = 0.86) by naïve Bayesian classifier. The experimental results demonstrated the potential of serial PET/CT scans in early prediction of anal tumor recurrence.

Long non-coding RNA expressed in macrophage co-varies with the inflammatory phenotype during macrophage development and polarization.

Advances in microarray, RNA-seq and omics techniques, thousands of long non-coding RNAs (lncRNAs) with unknown functions have been discovered. LncRNAs have presented a diverse perspective on gene regulation in diverse biological processes, especially in human immune response. Macrophages participate in the whole phase of immune inflammatory response. They are able to shape their phenotype and arouse extensive functional activation after receiving physiological and pathological stimuli. Emerging studies indicated that lncRNAs participated in the gene regulatory network during complex biological processes of macrophage, including macrophage-induced inflammatory responses. Here, we reviewed the existing knowledges of lncRNAs in the processes of macrophage development and polarization, and their roles in several different inflammatory diseases. Specifically, we focused on how lncRNAs function in macrophage, which might help to discover some potential therapeutic targets and diagnostic biomarkers.

Dynorphin activation of kappa opioid receptor protects against epilepsy and seizure-induced brain injury via PI3K/Akt/Nrf2/HO-1 pathway.

Dynorphins act as endogenous anticonvulsants via activation of kappa opioid receptor (KOR). However, the mechanism underlying the anticonvulsant role remains elusive. This study aims to investigate whether the potential protection of KOR activation by dynorphin against epilepsy was associated with the regulation of PI3K/Akt/Nrf2/HO-1 pathway. Here, a pilocarpine-induced rat model of epilepsy and Mg2+-free-induced epileptiform hippocampal neurons were established. Decreased prodynorphin (PDYN) expression, suppressed PI3K/Akt pathway, and activated Nrf2/HO-1 pathway were observed in rat epileptiform hippocampal tissues and in vitro neurons. Furthermore, dynorphin activation of KOR alleviated in vitro seizure-like neuron injury via activation of PI3K/Akt/Nrf2/HO-1 pathway. Further in vivo investigation revealed that PDYN overexpression by intra-hippocampus injection of PDYN-overexpressing lentiviruses decreased hippocampal neuronal apoptosis and serum levels of inflammatory cytokines and malondialdehyde (MDA) content, and increased serum superoxide dismutase (SOD) level, in pilocarpine-induced epileptic rats. The protection of PDYN in vivo was associated with the activation of PI3K/Akt/Nrf2/HO-1 pathway. In conclusion, dynorphin activation of KOR protects against epilepsy and seizure-induced brain injury, which is associated with activation of the PI3K/Akt/Nrf2/HO-1 pathway.

Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.

Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has been successfully applied to this task, leading to state-of-the-art performance. However, most of existing deep learning segmentation methods only work for a single imaging modality. PET/CT scanner is nowadays widely used in the clinic, and is able to provide both metabolic information and anatomical information through integrating PET and CT into the same utility. In this study, we proposed a novel multi-modality segmentation method based on a 3D fully convolutional neural network (FCN), which is capable of taking account of both PET and CT information simultaneously for tumor segmentation. The network started with a multi-task training module, in which two parallel sub-segmentation architectures constructed using deep convolutional neural networks (CNNs) were designed to automatically extract feature maps from PET and CT respectively. A feature fusion module was subsequently designed based on cascaded convolutional blocks, which re-extracted features from PET/CT feature maps using a weighted cross entropy minimization strategy. The tumor mask was obtained as the output at the end of the network using a softmax function. The effectiveness of the proposed method was validated on a clinic PET/CT dataset of 84 patients with lung cancer. The results demonstrated that the proposed network was effective, fast and robust and achieved significantly performance gain over CNN-based methods and traditional methods using PET or CT only, two V-net based co-segmentation methods, two variational co-segmentation methods based on fuzzy set theory and a deep learning co-segmentation method using W-net.

LncRNA TINCR is downregulated in diabetic cardiomyopathy and relates to cardiomyocyte apoptosis.

OBJECTIVE: LncRNA TINCR has been reported to be involved in cardiac hypertrophy, while its involvement in diabetic cardiomyopathy is unknown. MATERIALS AND METHODS: In this study, myocardial biopsy and serum collected from patients with diabetic cardiomyopathy, diabetic patients without cardiopathy and healthy controls, and the expression of TINCR in those tissues was detected by quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). ROC curve analysis was performed to evaluate the diagnostic value of TINCR expression for diabetic cardiomyopathy. Human cardiomyocyte cells (Cat# 12440053, Thermo Fisher Scientific) were treated with high glucose, and the expression of TINCR was detected by qRT-PCR. TINCR expression was transfected into cardiomyocyte cells and cell apoptosis under high glucose treated was detected by cell apoptosis assay. RESULTS: We found that TINCR expression level in myocardial biopsy and serum was significantly lower in patients with diabetic cardiomyopathy than in diabetic patients without cardiopathy and healthy controls, while no significant differences were found between diabetic patients without cardiopathy and healthy controls. TINCR expression level can be used to effective diagnose diabetic cardiomyopathy. High glucose treatment showed no significant effects on the expression of TINCR in human cardiomyocyte cells, and TINCR overexpression inhibited apoptosis of cardiomyocytes under high glucose treatment. CONCLUSION: Therefore, we conclude that lncRNA TINCR is downregulated in diabetic cardiomyopathy and it can inhibit cardiomyocyte apoptosis.

Statistical Iterative CBCT Reconstruction Based on Neural Network.

Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircase effect. Recently, various penalty terms with higher order differential operators were proposed to replace the TV penalty to avoid the staircase effect, at the cost of slightly blurring object edges. We developed a novel SIR algorithm using a neural network for CBCT reconstruction. We used a data-driven method to learn the "potential regularization term" rather than design a penalty term manually. This approach converts the problem of designing a penalty term in the traditional statistical iterative framework to designing and training a suitable neural network for CBCT reconstruction. We proposed using transfer learning to overcome the data deficiency problem and an iterative deblurring approach specially designed for the CBCT iterative reconstruction process during which the noise level and resolution of the reconstructed images may change. Through experiments conducted on two physical phantoms, two simulation digital phantoms, and patient data, we demonstrated the excellent performance of the proposed network-based SIR for CBCT reconstruction, both visually and quantitatively. Our proposed method can overcome the staircase effect, preserve both edges and regions with smooth intensity transition, and provide reconstruction results at high resolution and low noise level.