Comprehensive analysis of gene mutations and mismatch repair in Chinese colorectal cancer patients.

BACKGROUND: RAS, BRAF, and mismatch repair (MMR)/microsatellite instability (MSI) are crucial biomarkers recommended by clinical practice guidelines for colorectal cancer (CRC). However, their characteristics and influencing factors in Chinese patients have not been thoroughly described. AIM: To analyze the clinicopathological features of KRAS, NRAS, BRAF, and PIK3CA mutations and the DNA MMR status in CRC. METHODS: We enrolled 2271 Chinese CRC patients at the China-Japan Friendship Hospital. MMR proteins were tested using immunohistochemical analysis, and the KRAS/NRAS/BRAF/PIK3CA mutations were determined using quantitative polymerase chain reaction. Microsatellite status was determined using an MSI detection kit. Statistical analyses were conducted using SPSS software and logistic regression. RESULTS: The KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 44.6%, 3.4%, 3.7%, and 3.9% of CRC patients, respectively. KRAS mutations were more likely to occur in patients with moderate-to-high differentiation. BRAF mutations were more likely to occur in patients with right-sided CRC, poorly differentiated, or no perineural invasion. Deficient MMR (dMMR) was detected in 7.9% of all patients and 16.8% of those with mucinous adenocarcinomas. KRAS, NRAS, BRAF, and PIK3CA mutations were detected in 29.6%, 1.1%, 8.1%, and 22.3% of patients with dMMR, respectively. The dMMR was more likely to occur in patients with a family history of CRC, aged < 50 years, right-sided CRC, poorly differentiated histology, no perineural invasion, and with carcinoma in situ, stage I, or stage II tumors. CONCLUSION: This study analyzed the molecular profiles of KRAS, NRAS, BRAF, PIK3CA, and MMR/MSI in CRC, identifying key influencing factors, with implications for clinical management of CRC.

The persistence of SARS-CoV-2 in tissues and its association with long COVID symptoms: a cross-sectional cohort study in China.

BACKGROUND: Growing evidence suggests that symptoms associated with post-COVID-19 condition (also known as long COVID) can affect multiple organs and systems in the human body, but their association with viral persistence is not clear. The aim of this study was to investigate the persistence of SARS-CoV-2 in diverse tissues at three timepoints following recovery from mild COVID-19, as well as its association with long COVID symptoms. METHODS: This single-centre, cross-sectional cohort study was done at China-Japan Friendship Hospital in Beijing, China, following the omicron wave of COVID-19 in December, 2022. Individuals with mild COVID-19 confirmed by PCR or a lateral flow test scheduled to undergo gastroscopy, surgery, or chemotherapy, or scheduled for treatment in hospital for other reasons, at 1 month, 2 months, or 4 months after infection were enrolled in this study. Residual surgical samples, gastroscopy samples, and blood samples were collected approximately 1 month (18-33 days), 2 months (55-84 days), or 4 months (115-134 days) after infection. SARS-CoV-2 was detected by digital droplet PCR and further confirmed through RNA in-situ hybridisation, immunofluorescence, and immunohistochemistry. Telephone follow-up was done at 4 months post-infection to assess the association between the persistence of SARS-CoV-2 RNA and long COVID symptoms. FINDINGS: Between Jan 3 and April 28, 2023, 317 tissue samples were collected from 225 patients, including 201 residual surgical specimens, 59 gastroscopy samples, and 57 blood component samples. Viral RNA was detected in 16 (30%) of 53 solid tissue samples collected at 1 month, 38 (27%) of 141 collected at 2 months, and seven (11%) of 66 collected at 4 months. Viral RNA was distributed across ten different types of solid tissues, including liver, kidney, stomach, intestine, brain, blood vessel, lung, breast, skin, and thyroid. Additionally, subgenomic RNA was detected in 26 (43%) of 61 solid tissue samples tested for subgenomic RNA that also tested positive for viral RNA. At 2 months after infection, viral RNA was detected in the plasma of three (33%), granulocytes of one (11%), and peripheral blood mononuclear cells of two (22%) of nine patients who were immunocompromised, but in none of these blood compartments in ten patients who were immunocompetent. Among 213 patients who completed the telephone questionnaire, 72 (34%) reported at least one long COVID symptom, with fatigue (21%, 44 of 213) being the most frequent symptom. Detection of viral RNA in recovered patients was significantly associated with the development of long COVID symptoms (odds ratio 5·17, 95% CI 2·64-10·13, p<0·0001). Patients with higher virus copy numbers had a higher likelihood of developing long COVID symptoms. INTERPRETATION: Our findings suggest that residual SARS-CoV-2 can persist in patients who have recovered from mild COVID-19 and that there is a significant association between viral persistence and long COVID symptoms. Further research is needed to verify a mechanistic link and identify potential targets to improve long COVID symptoms. FUNDING: National Natural Science Foundation of China, National Key R&D Program of China, Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, and New Cornerstone Science Foundation. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Electroreductive Remote Benzylic C(sp3)-H Arylation of Aliphatic Ethers Using Cyanoarenes for the Synthesis of α-(Hetero)aryl Ethers.

An iodoarene-driven electroreductive remote C(sp3)-H arylation of unsymmetrical 1-(o-iodoaryl)alkyl ethers with cyanoarenes for the site selective synthesis of α-(hetero)aryl ethers is developed. With the introduction of cyanoarenes as both aryl sources and electron transfer mediators, this method includes an iodoarene-driven strategy to enable the regiocontrollable formation of two new bonds, one C(sp2)-H bond, and one C(sp2)-C(sp3) bond, in a single reaction step through the sequence of halogen atom transfer (XAT), hydrogen atom transfer (HAT), radical-radical coupling, and decyanation.

Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.

BACKGROUND: Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. METHOD: We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. RESULTS: Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). CONCLUSIONS: Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

Targeted IFNγ induction by a genetically engineered Salmonella typhimurium is the key to the liver metastasis inhibition in a mouse model of pancreatic neuroendocrine tumor.

Background: Liver metastasis is one of the primary causes of death for the patients with pancreatic neuroendocrine tumors (PNETs). However, no curative therapy has been developed so far. Methods: The anti-tumor efficacy of a genetically engineered tumor-targeting Salmonella typhimurium YB1 was evaluated on a non-functional INR1G9 liver metastasis model. Differential inflammatory factors were screened by Cytometric Bead Array. Antibody depletion assay and liver-targeted AAV2/8 expression vector were used for functional evaluation of the differential inflammatory factors. Results: We demonstrated that YB1 showed significant anti-tumor efficacy as a monotherapy. Since YB1 cannot infect INR1G9 cells, its anti-tumor effect was possibly due to the modulation of the tumor immune microenvironment. Two inflammatory factors IFNγ and CCL2 were elevated in the liver after YB1 administration, but only IFNγ was found to be responsible for the anti-tumor effect. Liver-targeted expression of IFNγ caused the activation of macrophages and NK cells, and reproduced the therapeutic effect of YB1 on liver metastasis. Conclusion: We demonstrated that YB1 may exhibit anti-tumor effect mainly based on IFNγ induction. Targeted IFNγ therapy can replace YB1 for treating liver metastasis of PNETs.

2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets.

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.

The Prevalence and Severity of School Bullying among Left-Behind Children: A Meta-Analysis.

The involvement of left-behind children (LBC) in school bullying has raised concern in China. However, the susceptibility of LBC to engage in bullying is controversial, and comprehensive, representative studies covering the entire country are lacking. The purpose of this study was to evaluate the prevalence and severity of school bullying among LBC. The Chinese National Knowledge Network, WanFang, VIP, PubMed, Web of Science, EMBASE, and EBSCO databases were searched for literature on being left-behind and bullying before April 2022. The effect size was measured by odds ratio (ORs), standard mean difference (SMD), and 95% confidence interval (CI). Random-effects or fixed-effects models were selected for meta-analysis, and subgroup analysis was used to explore the sources of heterogeneity. The meta-analysis included 25 studies of school bullying among LBC and non-LBC (NLBC). The prevalence of bullying perpetration and victimization among LBC were 18.58% (95% CI [3.72%, 33.44%], p < .05) and 40.62% (95% CI [25.47%, 55.78%], p < .05), respectively. Compared with NLBC, the risk of bullying perpetration and victimization among LBC increased 1.97 times (OR = 1.97, 95% CI [1.77, 2.20], p < .05) and 2.17 times (OR = 2.17, 95% CI [1.43, 3.29], p < .05), respectively. The severity of bullying experienced by LBC was higher than that of NLBC (SMD = 0.49, 95% CI [0.20, 0.79], p < .05). The prevalence and severity of school bullying were higher in LBC than in NLBC, and left-behindness was positively associated with school bullying. LBC are a crucial population to protect when developing bullying interventions.

Effectiveness of Home Care Interventions in Patients with Moderate to Severe Craniocerebral Injury Combined with Epilepsy.

Objective: To investigate the effect of a home care model on coping style and quality of life of patients with postcranial surgery complicated by epilepsy. Methods: One hundred and forty-four patients with postcranial surgery complicated by epilepsy admitted to our hospital from July 2017 to April 2018 were selected as study subjects and randomly divided into 63 cases each in the observation group and the control group. The control group was given nursing interventions including health education, psychological intervention, medication guidance, complication prevention, and follow-up management, while the observation group was jointly given collaborative family nursing interventions. At a follow-up of 6 months, indicators such as coping style, treatment compliance, and quality of life were compared between the two groups. Results: Patients in the observation group had significantly higher problem solving, help seeking, and rationalization scores and significantly lower self-blame scores than the control group (P < 0.01); significantly higher treatment compliance than the control group (P < 0.01); and significantly higher social functioning, emotional well-being, and energy/fatigue scores than the control group (P < 0.01). Conclusion: The home care model helps to promote the development of positive coping styles, improve treatment compliance, and improve the quality of life of patients after cranial surgery.

Cancer stem cells in TNBC.

Triple-negative breast cancer (TNBC) is a broad collection of breast cancer that tests negative for estrogen receptors (ER), progesterone receptors (PR), and excess human epidermal growth factor receptor 2 (HER2) protein. TNBC is considered to have poorer prognosis than other types of breast cancer because of a lack of effective therapeutic targets. The success of precision cancer therapies relies on the clarification of key molecular mechanisms that drive tumor growth and metastasis; however, TNBC is highly heterogeneous in terms of their cellular lineage composition and the molecular nature within each individual case. In particular, the rare and sometimes slow cycling cancer stem cells (CSCs) can provide effective means for TNBC to resist various treatments. Single cell analysis technologies, including single-cell RNA-seq (scRNA-seq) and proteomics, provide an avenue to unravel patient-level intratumoral heterogeneity by identifying CSCs populations, CSC biomarkers and the range of tumor microenvironment cellular constituents that contribute to tumor growth. This review discusses the emerging evidence for the role of CSCs in driving TNBC incidence and the therapeutic implications in manipulating molecular signaling against this rare cell population for the control of this deadly disease.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network.

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images.

BACKGROUND: Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE: This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS: We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS: The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model's attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS: Our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.

New one-step model of breast tumor locating based on deep learning.

BACKGROUND: Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) image is still a challenging work in computer-aided diagnosis (CAD). OBJECTIVE: This study aims to develop a novel automated algorithm for breast tumor detection based on deep learning. METHODS: We proposed a new deep learning network named One-step model which have one input and two outputs, the first one was the segmentation result and the other one was used for false-positive reduction. The proposed One-step model includes three key components: Base-net, Seg-net, and Cls-net based on Anchor Box. The model chose DenseNet to construct Base-net, the decoder part of RefineNet as Seg-net, and connected several middle layers of Base-net and Seg-net to Cls-net. From the first output acquired by Base-net and Seg-net, the model detected a series of suspicious lesion regions. Then the second output from the Cls-net was used to recognize and reduce the false-positive regions. RESULTS: Experimental results showed that the new model achieved competitive detection result with 90.78% F1 score, which was 8.55% higher than Single Shot MultiBox Detector (SSD) method. In addition, running new model is also computational efficient and has comparative cost effect as SSD. CONCLUSIONS: We established a novel One-step model which improves location accuracy by generating more precise bounding box via Seg-net and removing false targets by another object detection network (Cls-net). On the other hand, a real-time detection of tumor is achieved by sharing the common Base-net. The experimental results showed that the new model performed well on various irregular and blurred ultrasound images. As a result, this study demonstrated feasibility of applying deep learning scheme to detect breast lesions depicting on US image.

Adipocyte-derived stem cell-based gene therapy upon adipogenic differentiation on microcarriers attenuates type 1 diabetes in mice.

BACKGROUND: Insulin replenishment is critical for patients with type 1 diabetes; however, current treatments such as pancreatic islet transplantation and insulin injection are not ideal. In addition to stem cell or gene therapy alone, stem cell combined with gene therapy may provide a new route for insulin replenishment, which could avoid an autoimmune reaction against differentiated ß cells or systematic viral vector injection. METHODS: In this study, human adipocyte-derived stem cells (ADSCs) were transducted with lentiviral vectors expressing a furin-cleavable insulin gene. The expression levels of insulin were measured before and after adipogenic differentiation in the presence or absence of an adipocyte-specific promoter AP2. In vitro proliferation and in vivo survival of cells were examined on cytodex and cytopore microcarriers. The effect of ADSC-based gene therapy upon adipogenic differentiation on microcarriers was evaluated in the streptozotocin-induced type 1 diabetic mouse model. RESULTS: We found that differentiation of ADSCs into adipocytes increased insulin expression under the EF1 promoter, while adipocyte-specific AP2 promoter further increased insulin expression upon differentiation. The microcarriers supported cell attachment and proliferation during in vitro culture and facilitate cell survival after transplantation. Functional cells on the cytopore 1 microcarrier formed tissue-like structures and alleviated hyperglycemia in the type 1 diabetic mice after subcutaneous injection. CONCLUSIONS: Our results indicated that differentiation of ADSC and tissue-specific promotors may enhance the expression of therapeutic genes. The use of microcarriers may facilitate cell survival after transplantation and hold potential for long-term cell therapy.

Apatinib inhibits tumor growth and angiogenesis in PNET models.

Angiogenesis has a pivotal role in the growth and metastasis of pancreatic neuroendocrine tumors (PNETs). Apatinib inhibits angiogenesis as a highly selective KDR inhibitor and has been used to treat advanced gastric cancer and malignancies in clinical settings. However, the efficacy of apatinib in PNETs remains unclear. The aim of this study was to compare the antitumor efficacy of apatinib with that of the standard PNET drug sunitinib in our subcutaneous and liver metastasis models of insulinoma and non-functional PNET. Our results revealed that apatinib had a generally comparable or even superior antitumor effect to that of sunitinib on primary PNET, and it inhibited angiogenesis without directly causing tumor cell cytotoxicity. Apatinib inhibited the tumor in a dose-dependent manner, and the high dose was well tolerated in mice. We also found that the apatinib efficacy in liver metastasis models was cell-type (disease) selective. Although apatinib efficiently inhibited INR1G9-represented non-functional PNET liver metastasis, it led to the emergence of a hypoxic area in the INS-1-represented insulinoma and promoted liver metastasis. Our study demonstrated that apatinib has promise for clinical applications in certain malignant PNETs, and the application of anti-angiogenesis drugs to benign insulinomas may require careful consideration.

[Extraction of calcification in ultrasonic images based on convolution neural network].

Ultrasound is the best way to diagnose thyroid nodules. To discriminate benign and malignant nodules, calcification is an important characteristic. However, calcification in ultrasonic images cannot be extracted accurately because of capsule wall and other internal tissue. In this paper, deep learning was first proposed to extract calcification, and two improved methods were proposed on the basis of Alexnet convolutional neural network. First, adding the corresponding anti-pooling (unpooling) and deconvolution layers (deconv2D) made the network to be trained for the required features and finally extract the calcification feature. Second, modifying the number of convolution templates and full connection layer nodes made feature extraction more refined. The final network was the combination of two improved methods above. To verify the method presented in this article, we got 8 416 images with calcification, and 10 844 without calcification. The result showed that the accuracy of the calcification extraction was 86% by using the improved Alexnet convolutional neural network. Compared with traditional methods, it has been improved greatly, which provides effective means for the identification of benign and malignant thyroid nodules.

A novel TIRADS of US classification.

BACKGROUND: Thyroid imaging reporting and data system (TIRADS) is the assessment of a risk stratification of thyroid nodules, usually using a score. However, there is no consensus as to the version of TIRADS for reporting the results of thyroid ultrasound in clinic. The objective of this study is to develop a practical TIRADS with which to categorize thyroid nodules and stratify their malignant risk. METHODS: A TIRADS scoring system was developed to provide more decision levels than standard scoring through the selection of the ultrasound features which include the calcification shape, margins, taller-than-wide, internal echo, blood flow quantization of features, setting of the weight, and calculation of the score. Ultimately, the accuracy of our TIRADS was evaluated by comparing with the results of current vision of TIRADS and thyroid radiologist in 153 patients who had US-guided fine-needle aspiration biopsy. RESULTS: Classification results showed that the total accuracy reached 97% (100% of malignant and 95% of the benign) in 153 cases (benign:78, malignant:75). The percentages of malignancy is defined in our TIRADS were as follows: TIRADS 2 (0% malignancy), TIRADS 3 (3.6% malignancy), TIRADS 4 (17-75% malignancy), and TIRADS 5 (98% malignancy). CONCLUSIONS: We established a novel TIRADS to predict the malignancy risk of the thyroid nodules based on six categories US features by a scoring system, which included a standardized vocabulary and score and a quantified risk assessment. The results showed that objective quantitative classification of thyroid nodules by our TIRADS can be useful in guiding management decisions.

Pseudo-hemorrhagic region formation in pancreatic neuroendocrine tumors is a result of blood vessel dilation followed by endothelial cell detachment.

Aberrant blood vessel formation and hemorrhage may contribute to tumor progression and are potential targets in the treatment of several types of cancer. Pancreatic neuroendocrine tumors (PNETs) are highly vascularized, particularly when they are well-differentiated. However, the process of vascularization and endothelial cell detachment in PNETs is poorly understood. In the present study, 132 PNET clinical samples were examined and a special type of hemorrhagic region was observed in ~30% of the samples regardless of tumor subtype. These hemorrhagic regions were presented as blood-filled caverns with a smooth boundary and were unlined by endothelial cells. Based on the extensive endothelial cell detachment observed in the clinical samples, the formation process of these blood-filled caverns was hypothesized. Blood vessel dilation followed by detachment of endothelial cells from the surrounding tumor tissue was speculated. This was further supported using an INS-1 xenograft insulinoma model. As the formation process was distinct from the typical diffusive hemorrhage, it was named 'pseudo-hemorrhage'. Furthermore, it was demonstrated that epithelial (E-) cadherin and ß-catenin were overexpressed in tumor cells surrounding these pseudo-hemorrhagic regions. Therefore, even though no statistically significant association of pseudo-hemorrhage with clinical features (metastasis or disease recurrence) was identified, the high levels of E-cadherin and ß-catenin expression may suggest that a number of features of normal islet cells are retained.

Neural Bases of Unconscious Error Detection in a Chinese Anagram Solution Task: Evidence from ERP Study.

In everyday life, error monitoring and processing are important for improving ongoing performance in response to a changing environment. However, detecting an error is not always a conscious process. The temporal activation patterns of brain areas related to cognitive control in the absence of conscious awareness of an error remain unknown. In the present study, event-related potentials (ERPs) in the brain were used to explore the neural bases of unconscious error detection when subjects solved a Chinese anagram task. Our ERP data showed that the unconscious error detection (UED) response elicited a more negative ERP component (N2) than did no error (NE) and detect error (DE) responses in the 300-400-ms time window, and the DE elicited a greater late positive component (LPC) than did the UED and NE in the 900-1200-ms time window after the onset of the anagram stimuli. Taken together with the results of dipole source analysis, the N2 (anterior cingulate cortex) might reflect unconscious/automatic conflict monitoring, and the LPC (superior/medial frontal gyrus) might reflect conscious error recognition.

Colocalization of insulin and glucagon in insulinoma cells and developing pancreatic endocrine cells.

A significant portion of human and rat insulinomas coexpress multiple hormones. This character termed as multihormonality is also observed in some early pancreatic endocrine cells which coexpress insulin and glucagon, suggesting an incomplete differentiation status of both cells. Here we demonstrate that insulinoma cells INS-1 and INS-1-derived single cell clone INS-1-15 coexpressed insulin and glucagon in a portion of cells. These two hormones highly colocalized in the intracellular vesicles within a cell. Due to the existence of both PC1/3 and PC2 in INS-1-derived cells, proglucagon could be processed into glucagon, GLP-1 and GLP-2. These glucagon-family peptides and insulin were secreted simultaneously corresponding to the elevating glucose concentrations. The coexpression and partial colocalization of insulin and glucagon was also observed in rat fetal pancreatic endocrine cells, but the colocalization rate was generally lower and more diverse, suggesting that in the developing pancreatic endocrine cells, insulin and glucagon may be stored in nonidentical pools of secreting vesicles and might be secreted discordantly upon stimulus.