Whole-brain mapping of monosynaptic afferent inputs to the CRH neurons in the medial prefrontal cortex of mice.

Corticotropin-releasing hormone (CRH) neurons are densely distributed in the medial prefrontal cortex (mPFC), which plays a crucial role in integrating and processing emotional and cognitive inputs from other brain regions. Therefore, it is important to know the neural afferent patterns of mPFCCRH neurons, which are still unclear. Here, we utilized a rabies virus-based monosynaptic retrograde tracing system to map the presynaptic afferents of the mPFCCRH neurons throughout the entire brain. The results show that the mPFCCRH neurons receive inputs from three main groups of brain regions: (1) the cortex, primarily the orbital cortex, somatomotor areas, and anterior cingulate cortex; (2) the thalamus, primarily the anteromedial nucleus, mediodorsal thalamic nucleus, and central medial thalamic nucleus; and (3) other brain regions, primarily the basolateral amygdala, hippocampus, and dorsal raphe nucleus. Taken together, our results are valuable for further investigations into the roles of the mPFCCRH neurons in normal and neurological disease states. These investigations can shed light on various aspects such as cognitive processing, emotional modulation, motivation, sociability, and pain.

Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer.

BACKGROUND: Accurate preoperative clinical staging of gastric cancer helps determine therapeutic strategies. However, no multi-category grading models for gastric cancer have been established. This study aimed to develop multi-modal (CT/EHRs) artificial intelligence (AI) models for predicting tumor stages and optimal treatment indication based on preoperative CT images and electronic health records (EHRs) in patients with gastric cancer. METHODS: This retrospective study enrolled 602 patients with a pathological diagnosis of gastric cancer from Nanfang hospital retrospectively and divided them into training (n = 452) and validation sets (n = 150). A total of 1326 features were extracted of which 1316 radiomic features were extracted from the 3D CT images and 10 clinical parameters were obtained from electronic health records (EHRs). Four multi-layer perceptrons (MLPs) whose input was the combination of radiomic features and clinical parameters were automatically learned with the neural architecture search (NAS) strategy. RESULTS: Two two-layer MLPs identified by NAS approach were employed to predict the stage of the tumor showed greater discrimination with the average ACC value of 0.646 for five T stages, 0.838 for four N stages than traditional methods with ACC of 0.543 (P value = 0.034) and 0.468 (P value = 0.021), respectively. Furthermore, our models reported high prediction accuracy for the indication of endoscopic resection and the preoperative neoadjuvant chemotherapy with the AUC value of 0.771 and 0.661, respectively. CONCLUSIONS: Our multi-modal (CT/EHRs) artificial intelligence models generated with the NAS approach have high accuracy for tumor stage prediction and optimal treatment regimen and timing, which could facilitate radiologists and gastroenterologists to improve diagnosis and treatment efficiency.

Epigenetic alternations of microRNAs and DNA methylation contribute to gestational diabetes mellitus.

This study aimed to identify epigenetic alternations of microRNAs and DNA methylation for gestational diabetes mellitus (GDM) diagnosis and treatment using in silico approach. Data of mRNA and miRNA expression microarray (GSE103552 and GSE104297) and DNA methylation data set (GSE106099) were obtained from the GEO database. Differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs) and differentially methylated genes (DMGs) were obtained by limma package. Functional and enrichment analyses were performed with the DAVID database. The protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Simultaneously, a connectivity map (CMap) analysis was performed to screen potential therapeutic agents for GDM. In GDM, 184 low miRNA-targeting up-regulated genes and 234 high miRNA-targeting down-regulated genes as well as 364 hypomethylation-high-expressed genes and 541 hypermethylation-low-expressed genes were obtained. They were mainly enriched in terms of axon guidance, purine metabolism, focal adhesion and proteasome, respectively. In addition, 115 genes (67 up-regulated and 48 down-regulated) were regulated by both aberrant alternations of miRNAs and DNA methylation. Ten chemicals were identified as putative therapeutic agents for GDM and four hub genes (IGF1R, ATG7, DICER1 and RANBP2) were found in PPI and may be associated with GDM. Overall, this study identified a series of differentially expressed genes that are associated with epigenetic alternations of miRNA and DNA methylation in GDM. Ten chemicals and four hub genes may be further explored as potential drugs and targets for GDM diagnosis and treatment, respectively.

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs. METHODS: The ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samples t tests and analysis of variance were employed for statistical analysis. RESULTS: ODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p = 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients. CONCLUSION: The ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC. KEY POINTS: • A fully automated deep-learning method (ODS net) is developed to detect and segment OARs in clinical CT images. • This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning. • This deep-learning-based framework delineating a single image requires approximately 30 s, which is suitable for clinical workflows.

miR-638 regulates differentiation and proliferation in leukemic cells by targeting cyclin-dependent kinase 2.

MicroRNAs have been extensively studied as regulators of hematopoiesis and leukemogenesis. We identified miR-638 as a novel regulator in myeloid differentiation and proliferation of leukemic cells. We found that miR-638 was developmentally up-regulated in cells of myeloid but not lymphoid lineage. Furthermore, significant miR-638 down-regulation was observed in primary acute myeloid leukemia (AML) blasts, whereas miR-638 expression was dramatically up-regulated in primary AML blasts and leukemic cell lines undergoing forced myeloid differentiation. These observations suggest that miR-638 might play a role in myeloid differentiation, and its dysregulation may contribute to leukemogenesis. Indeed, ectopic expression of miR-638 promoted phorbol 12-myristate 13-acetate- or all-trans-retinoic acid-induced differentiation of leukemic cell lines and primary AML blasts, whereas miR-638 inhibition caused an opposite phenotype. Consistently, miR-638 overexpression induced G1 cell cycle arrest and reduced colony formation in soft agar. Cyclin-dependent kinase 2 (CDK2) was found to be a target gene of miR-638. CDK2 inhibition phenotypically mimicked the overexpression of miR-638. Moreover, forced expression of CDK2 restored the proliferation and the colony-forming ability inhibited by miR-638. Our data suggest that miR-638 regulates proliferation and myeloid differentiation by targeting CDK2 and may serve as a novel target for leukemia therapy or marker for AML diagnosis and prognosis.

Protein markers related to vascular responsiveness after hemorrhagic shock in rats.

BACKGROUND: Vascular hyporesponsiveness is an important pathophysiological feature of some critical conditions such as hemorrhagic shock. Many proteins and molecules are involved in the regulation of the pathologic process, however the mechanism has still remained unclear. Our study was intended to look for the related protein markers involved in the regulation of vascular reactivity after hemorrhagic shock. METHODS: Differential in-gel electrophoresis and tandem mass spectrometry were applied to quantify the differences of protein expression in the superior mesenteric arteries from hemorrhagic shock and normal rats. RESULTS: A total of 2317 differentially expressed protein spots in the superior mesenteric arteries of rats before and after hemorrhagic shock were found, and 146 protein spots were selected for tandem mass spectrometry identification. Thirty-seven differentially expressed proteins were obtained, including 3 uncharacterized proteins and 34 known proteins. Among them, heat shock protein beta-1 and calmodulin were the known proteins involved in the occurrence of vascular hyporesponsiveness. Bioinformatics analysis results showed that 18 proteins were related to vasoconstriction, 11 proteins may be involved in other vascular functions such as regulation of angiogenesis and endothelial cell proliferation. CONCLUSIONS: The changes of vascular responsiveness after hemorrhagic shock in rats may be associated with the upregulation or downregulation of previously mentioned protein expressions. These findings may provide the basis for understanding and further study of the mechanism and treatment targets of vascular hyporeactivity after shock.

Beneficial effects of platelet-derived growth factor on hemorrhagic shock in rats and the underlying mechanisms.

Studies have shown that local application of platelet-derived growth factor (PDGF) can be used for the treatment of acute and chronic wounds. We investigated if systemic application of PDGF has a protective effect on acute hemorrhagic shock in rats in the present study. Using hemorrhagic shock rats and isolated superior mesenteric arteries, the effects of PDGF-BB on hemodynamics, animal survival, and vascular reactivity as well as the roles of the gap junction proteins connexin (Cx)40 and Cx43, PKC, and Rho kinase were observed. PDGF-BB (1­15 µg/kg iv) significantly improved the hemodynamics and blood perfusion to vital organs (liver and kidney) as well as vascular reactivity and improved the animal survival in hemorrhagic shock rats. PDGF recovering shock-induced vascular hyporeactivity depended on the integrity of the endothelium and myoendothelial gap junction. Cx43 antisense oligodeoxynucleotide abolished these improving effects of PDGF, whereas Cx40 oligodeoxynucleotide did not. Further study indicated that PDGF increased the activity of Rho kinase and PKC as well as vascular Ca2+ sensitivity, whereas it did not interfere with the intracellular Ca2+ concentration in hypoxia-treated vascular smooth muscle cells. In conclusion, systemic application of PDGF-BB may exert beneficial effects on hemorrhagic shock, which are closely related to the improvement of vascular reactivity and hemodynamics. The improvement of PDGF-BB in vascular reactivity is vascular endothelium and myoendothelial gap junction dependent. Cx43, Rho kinase, and PKC play very important role in this process. These findings suggest that PDGF may be a potential measure to treat acute clinical critical diseases such as severe trauma, shock, and sepsis.

Topochemical cross-linking of diacetylene in a highly interpenetrated three-dimensional covalent organic framework.

Topochemical polymerization is a highly promising and effective method for constructing complex three-dimensional structures and functionalized polymers. Herein, we present for the first time a topochemical reaction of diacetylene within a meticulously designed covalent organic framework (ZNUC-1). Our experimental results revealed that ZNUC-1 underwent a 1,4-addition reaction under thermal induction, forming an ene-yne-conjugated structure.

Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis.

RATIONALE AND OBJECTIVES: To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). MATERIALS AND METHODS: We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT. CI and CP were evaluated using SDMT scores at baseline and follow-up. The included DIR sequence aided in identifying cortical lesions. Lesion radiomics and structural features were extracted and selected from multi-sequence MRI, followed by the computation of radiomics and structural scores. The nomogram was developed through multivariate logistic regression, integrating clinical data, radiomics, and structural scores to identify CI in patients. Moreover, a similar method was employed to further construct a nomogram predicting CP in patients. RESULTS: The nomogram demonstrated superior performance in identifying patients with CI, with area under the curve (AUC) values of 0.937 (95% Conf. Interval: 0.898-0.975) and 0.876 (0.810-0.943) in internal and external validation sets, compared to models solely based on clinical data, lesion radiomics, and structural features. Furthermore, another nomogram constructed in predicting CP also exhibited outstanding performance, with an AUC value of 0.969 (0.875-1.000) in the validation set. CONCLUSION: These nomograms, integrating clinical data, multi-sequence lesions radiomics, and structural features, enable more effective identification of CI and early prediction of CP in RRMS patients, providing important support for clinical decision-making.

Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan.

Objective. Accurate segmentation of the pancreas from abdomen CT scans is highly desired for diagnosis and treatment follow-up of pancreatic diseases. However, the task is challenged by large anatomical variations, low soft-tissue contrast, and the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a new segmentation network and a semi-supervised learning framework to alleviate the lack of annotated images and improve the accuracy of segmentation.Approach.In this paper, we propose a novel graph-enhanced pancreas segmentation network (GEPS-Net), and incorporate it into a semi-supervised learning framework based on iterative uncertainty-guided pseudo-label refinement. Our GEPS-Net plugs a graph enhancement module on top of the CNN-based U-Net to focus on the spatial relationship information. For semi-supervised learning, we introduce an iterative uncertainty-guided refinement process to update pseudo labels by removing low-quality and incorrect regions.Main results.Our method was evaluated by a public dataset with four-fold cross-validation and achieved the DC of 84.22%, improving 5.78% compared to the baseline. Further, the overall performance of our proposed method was the best compared with other semi-supervised methods trained with only 6 or 12 labeled volumes.Significance.The proposed method improved the segmentation performance of the pancreas in CT images under the semi-supervised setting. It will assist doctors in early screening and making accurate diagnoses as well as adaptive radiotherapy.

Corticotropin-releasing factor is involved in acute stress-induced analgesia and antipruritus.

BACKGROUND: Under the condition of stress, the hypothalamic-pituitary-adrenal axis (HPA axis) is activated and causes the secretion of corticotropin-releasing factor (CRF). Previous studies have demonstrated that CRF is involved in the regulation of pain and itch. Thus, it remains worthy to explore whether the desensitization of pain and itch under high-intensity acute stress (such as high fear and tension) is related to the sharp increase of CRF. METHODS: Forced swimming was used to simulate acute stress. ELISA and pharmacological methods were conducted to observe the effects of forced swimming on acute pain or itch and the relationship between blood CRF content and itch or pain behavior. Intracerebroventricular (ICV) administration of CRF was conducted to examine the effects of CRF on acute pain or itch. Intrathecal administration of CRF receptor agonist or antagonist was conducted to examine the receptor mechanisms of the regulatory role of CRF in pain and itch. RESULTS: ELISA experiment showed that the serum CRF in mice reached its peak within 5-10 min after acute stress (forced swimming). Behavioral data showed that the scratching behavior induced by itch agents decreased after acute swimming, while the mechanical pain threshold increased significantly. The inhibitory effect of acute stress on pain and itch is mediated by CRF receptor2 (CRFR2). Then, ICV injection of CRF was used to simulate the massive release of CRF under acute stress, and we observed that the scratching behavior induced by histamine or chloroquine was significantly inhibited after ICV injection of CRF. The above effects of CRF are mainly mediated by CRFR2. These results suggest that 5-10 min after acute stress, a large amount of CRF is released into the blood from the hypothalamus, which significantly inhibits acute pain and itch by acting on CRFR2. ICV injection of CRF can replicate the antipruritus effects of acute stress. CONCLUSIONS: The present study investigated the mechanism of acute stress-induced analgesia and antipruritus and provided theoretical support for the treatment of pain and itch.

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

Effects of crab disturbance on nitrogen migration and transformation in a coastal tidal flat wetland.

The influence of crab disturbances on nitrogen migration and the transformations of pore water and overlying water in a coastal tidal flat wetland were investigated at the lab scale, and the nitrogen exchange flux at the sediment-water interface was calculated. The results showed that crabs, combined with tidal effects, had significant effects on the microtopography of the studied crab box. In addition, there was no significant difference in the concentrations of NH4+-N, NO3--N, or TN between two points in the horizontal direction (P > 0.05), and there were significant differences in the concentrations of NH4+-N and TN in the vertical direction (P < 0.05); the NO3--N concentration difference was not obvious (P > 0.05). The NO3--N concentration in the surface pore water of the crab box had a downward trend with time. Furthermore, the NH4+-N and TN contents in the overlying water in the crab box were significantly higher than those of the control box, indicating that crab disturbances also had significant effects on the concentrations of NH4+-N, NO3--N, and TN in the overlying water. The existence of crab caves greatly promoted the nitrogen exchange flux at the sediment-water interface, and the mean exchange fluxes of NH4+-N, NO3--N and TN were 51.40 mmol (m2 day)-1, -13.44 mmol (m2 day)-1 and 39.74 mmol (m2 day)-1, respectively (much higher than those measured in the control box), implying that NH4+-N and TN were released from the sediment to the overlying water, while NO3--N was released from the overlying water to the sediment.

Resolution enhancement for lung 4D-CT based on transversal structures by using multiple Gaussian process regression learning.

PURPOSE: Four-dimensional computed tomography (4D-CT) plays a useful role in many clinical situations. However, due to the hardware limitation of system, dense sampling along superior-inferior direction is often not practical. In this paper, we develop a novel multiple Gaussian process regression model to enhance the superior-inferior resolution for lung 4D-CT based on transversal structures. METHODS: The proposed strategy is based on the observation that high resolution transversal images can recover missing pixels in the superior-inferior direction. Based on this observation and motived by random forest algorithm, we employ multiple Gaussian process regression model learned from transversal images to improve superior-inferior resolution. Specifically, we first randomly sample 3 × 3 patches from original transversal images. The central pixel of these patches and the eight-neighbour pixels of their corresponding degraded versions form the label and input of training data, respectively. Multiple Gaussian process regression model is then built on the basis of multiple training subsets obtained by random sampling. Finally, the central pixel of the patch is estimated based on the proposed model, with the eight-neighbour pixels of each 3 × 3 patch from interpolated superior-inferior direction images as inputs. RESULTS: The performance of our method is extensively evaluated using simulated and publicly available datasets. Our experiments show the remarkable performance of the proposed method. CONCLUSIONS: In this paper, we propose a new approach to improve the 4D-CT resolution, which does not require any external data and hardware support, and can produce clear coronal/sagittal images for easy viewing.

Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning.

This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet). The ResNet model was developed based on contrast-enhanced computed tomography (CE-CT) in a training cohort consisted of 80 patients pathologically diagnosed gastrointestinal sromal tumors (GISTs) and validated in internal and external validation cohort respectively. Independent clinicopathologic factors were integrated with the ResNet model to construct the individualized nomogram. The performance of the nomogram was evaluated in regard to discrimination, calibration, and clinical usefulness. The ResNet model was significantly associated with RFS. Integrable predictors in the individualized ResNet nomogram included the tumor site, size, and mitotic count. Compared with modified NIH, AFIP, and clinicopathologic nomogram, both ResNet nomogram and ResNet model showed a better discrimination capability with AUCs of 0·947(95%CI, 0·910-0·984) for 3-year-RFS, 0·918(0·852-0·984) for 5-year-RFS, and AUCs of 0·912 (0·851-0·973) for 3-year-RFS, 0·887(0·816-0·960) for 5-year-RFS, respectively. Calibration curve shows the good calibration of the nomogram in terms of the agreement between the estimated and the observed 3- and 5- year outcomes. Decision curve analysis showed that the ResNet nomogram had a higher overall net benefit. In conclusion, we presented a deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance and could be a potential tool to select patients for adjuvant imatinib therapy.

Differences in expression of retinal proteins between diabetic and normal rats.

AIM: To compare and identify the differences in expression of retinal proteins between normal and diabetic rats, and to analyze the molecular pathogenetic mechanisms of retinal diseases caused by diabetes. METHODS: Changes in protein expression of retinal tissues from diabetic and normal rats were observed using 2-dimensional polyacrylamide gel electrophoresis (2-DE). Some protein spots exhibiting statistically significant variations (P<0.05) were selected randomly and identified by tandem mass spectrometry and analyzed by bioinformatics. RESULTS: 2-DE showed that the expression was up-regulated in 5 retinal proteins, down-regulated in 23 retinal proteins, and disappeared in 8 retinal proteins. Eight spots were identified from the 36 spots by tandem mass spectrometry (MS/MS) and analyzed by bioinformatics. Guanylate kinase 1, triosephosphate isomerase 1, ATP synthase subunit d, albumin and dimethylarginine dimethylaminohydrolase 2 played an important role in signal transduction. Triosephosphate isomerase 1, crystallin alpha B, ATP synthase subunit d and peroxiredoxin 6 were involved in energy metabolism of retinal tissues. Guanylate kinase 1 played an important role in photoexcitation of retinal rod photoreceptor cells. Whether crystallin beta A1 plays a role in diabetic retinas is unknown so far. CONCLUSION: There are differences in expression of retinal proteins between diabetic and normal rats. These proteins may be involved in the mechanisms and prognosis of retinal diseases caused by diabetes.

Application of two-dimensional electrophoresis in the research of retinal proteins of diabetic rat.

Diabetes mellitus (DM) is a chronic disease which is associated with numerous serious health complications such as diabetic retinopathy, and is the leading cause of new cases of blindness in adults at the age of 20-74 years old. The aim of the study was to establish and optimize a two-dimensional polyacrylamide gel electrophoresis (2-DE) technique for retina proteomics to improve the resolution and reproducibility, and to observe the proteomic changes of retinal tissues in diabetic and normal rats. Proteins were extracted from retinal tissues of normal and 8 weeks diabetic SD rats and used in two-dimensional electrophoresis. Various conditions of retina proteomic 2-DE were adjusted, optimized and protein spots of differential expression were obtained through analysis of 2-DE images with PDQuest software. By choosing appropriate sample amount, using pre-cast IPG dry strips (pH 5-8) and casting 12% equal gel, satisfactory 2-DE images of retina were obtained and a steady 2-DE technique was established. In this way, we found 36 spots in 2-DE gel of diabetic retinas that exhibited statistically significant variations, including up-regulation of 5 proteins in diabetic rat retinas, down-regulation of 23, and disappearance of 8, in comparison with normal tissues. The differences of protein expression were observed in retinas between diabetic and normal rats. Our established 2-DE technique of retina proteins could be effectively applied in proteomics of retina diseases.

Comparative proteome analysis of breast cancer and adjacent normal breast tissues in human.

Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry (MALDI-TOF/TOF-MS), incorporated with online database searching, were performed to investigate differential proteins of breast cancer and adjacent normal breast tissues. Considering that serum albumin is abundantly presented in normal control samples, 15 differential spots detected in 11 out of 12 (91.7%) breast cancer samples were identified by online SIENA-2DPAGE database searching and MALDI-TOF/TOF-MS analysis. The results indicate that pathological changes of breast cancer are concerned with augmentation of substance metabolism, promotion of proteolytic activity, decline of activity of some inhibitors of enzymes, and so on. Some important proteins involved in the pathological process of breast cancer with changed expression may be useful biomarkers, such as alpha-1-antitrypsin, EF-1-beta, cathepsin D, TCTP, SMT3A, RPS12, and PSMA1, among which SMT3A, RPS12, and PSMA1 were first reported for breast cancer in this study.

Down-regulation of alpha-2u globulin in renal mitochondria of STZ-induced diabetic rats observed by a proteomic method.

AIM: To identify the changes of mitochondrial protein expression in diabetic renal parenchyma and to characterize their molecular functions and biological processes in diabetes. METHODS: Mitochondrial proteins extracted from renal parenchyma mitochondria of streptozotocin-induced diabetic rats and normal rats were separated by two-dimensional polyacrylamide gel electrophoresis and identified by matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry. RESULTS: Eleven proteins from 533 visualized protein spots displayed significant different expressions in mitochondria of diabetic kidneys compared with those in normal ones. Among these altered proteins, two proteins with the most obvious changes in protein expression were identified as alpha-2u globulin (mature protein, named A2) and its proteolytically modified form (named A2-fragment) respectively. These proteins were found in mitochondria of male rat renal parenchyma and were proved to be down-regulated in diabetic rats simultaneously. CONCLUSION: Our results suggest that down-regulation of alpha-2u globulin may be associated with an abnormal ß-oxidation of long-chain fatty acids during diabetes. The decreased expression of A2-fragment in renal mitochondria of diabetic nephropathy may reduce fatty acid ß-oxidation, which leads to a diminished energy supply from mitochondria to kidney tissue and the deposition of a large number of fatty acids in the kidney, ultimately causing and aggravating kidney damage. In conclusion, these findings may be helpful for understanding the molecular mechanism of diabetic nephropathy.