Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms.

BACKGROUND AND OBJECTIVE: Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions. METHODS: In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5-fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast. RESULTS: The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbreast dataset. CONCLUSION: The capability of the YOLO detector boosted the classification models to achieve a promising breast lesion diagnostic performance. Such prediction results should help to develop a feasible CAD system for practical breast cancer diagnosis.

Computer-aided osteoporosis detection from DXA imaging.

BACKGROUND AND OBJECTIVE: Osteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan. METHODS: The CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements. RESULTS: During a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems' Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R2 = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R2 = 0.9709. CONCLUSIONS: The CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.

Automatic hip geometric feature extraction in DXA imaging using regional random forest.

BACKGROUND: Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone mineral density has some notable limitations in terms of hip fracture risk assessment. OBJECTIVES: To learn more about bone strength, hip geometric features (HGFs) can be collected. However, organizing a hip fracture risk study for a large population using a manual HGFs collection technique would be too arduous to be practical. Thus, an automatic HGFs extraction technique is needed. METHOD: This paper presents an automated HGFs extraction technique using regional random forest. Regional random forest localizes landmark points from femur DXA images using local constraints of hip anatomy. The local region constraints make random forest robust to noise and increase its performance because it processes the least number of points and patches. RESULTS: The proposed system achieved an overall accuracy of 96.22% and 95.87% on phantom data and real human scanned data respectively. CONCLUSION: The proposed technique's ability to measure HGFs could be useful in research on the cause and facts of hip fracture and could help in the development of new guidelines for hip fracture risk assessment in the future. The technique will reduce workload and improve the use of X-ray devices.

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies. In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.

Femur segmentation in DXA imaging using a machine learning decision tree.

BACKGROUND: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue. Reasons for this failure include low contrast and noise in DXA images, bone shape variability, and inconsistent X-ray beam penetration and attenuation, which cause shadowing effects and person-to-person variation. OBJECTIVE: To present a new method namely, a Pixel Label Decision Tree (PLDT), and test whether it can achieve higher accurate performance in femur segmentation in DXA imaging. METHODS: PLDT involves mainly feature extraction and selection. Unlike photographic images, X-ray images include features on the surface and inside an object. In order to reveal hidden patterns in DXA images, PLDT generates seven new feature maps from existing high energy (HE) and low energy (LE) X-ray features and determines the best feature set for the model. The performance of PLDT in femur segmentation is compared with that of three widely used medical image segmentation algorithms, the Global Threshold (GT), Region Growing Threshold (RGT), and artificial neural networks (ANN). RESULTS: PLDT achieved a higher accuracy of femur segmentation in DXA imaging (91.4%) than either GT (68.4%), RGT (76%) or ANN (84.4%). CONCLUSIONS: The study demonstrated that PLDT outperformed other conventional segmentation techniques in segmenting DXA images. Improved segmentation should help accurate computation of BMD which later improves clinical diagnosis of osteoporosis.

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.

BACKGROUND AND OBJECTIVE: Automatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma. METHODS: In this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN). The proposed FrCN method directly learns the full resolution features of each individual pixel of the input data without the need for pre- or post-processing operations such as artifact removal, low contrast adjustment, or further enhancement of the segmented skin lesion boundaries. We evaluated the proposed method using two publicly available databases, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets. To evaluate the proposed method, we compared the segmentation performance with the latest deep learning segmentation approaches such as the fully convolutional network (FCN), U-Net, and SegNet. RESULTS: Our results showed that the proposed FrCN method segmented the skin lesions with an average Jaccard index of 77.11% and an overall segmentation accuracy of 94.03% for the ISBI 2017 test dataset and 84.79% and 95.08%, respectively, for the PH2 dataset. In comparison to FCN, U-Net, and SegNet, the proposed FrCN outperformed them by 4.94%, 15.47%, and 7.48% for the Jaccard index and 1.31%, 3.89%, and 2.27% for the segmentation accuracy, respectively. Furthermore, the proposed FrCN achieved a segmentation accuracy of 95.62% for some representative clinical benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases in the ISBI 2017 test dataset, exhibiting better performance than those of FCN, U-Net, and SegNet. CONCLUSIONS: We conclude that using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.

Denoising images of dual energy X-ray absorptiometry using non-local means filters.

BACKGROUND: In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis. OBJECTIVE: This study aims to develop and test a new technology to improve the quality, remove the noise, and preserve the edges and fine details of real DXA images. METHODS: In this study, a denoising technique for high and low energy DXA images using a non-local mean filter (NLM) was presented. The source and detector noises of a DXA system were modeled for both high and low DXA images. Then, the optimized parameters of the NLM filter were derived utilizing the experimental data from CIRS-BFP phantoms. After that, the optimized NLM was tested and verified using the DXA images of the phantoms and real human spine and femur. RESULTS: Quantitative evaluation of the results showed average 24.22% and 34.43% improvement of the signal-to-noise ratio for real high and low spine images, respectively, while the improvements were about 15.26% and 13.55% for the high and low images of the femur. The qualitative visual observations of both phantom and real structures also showed significantly improved quality and reduced noise while preserving the edges in both high and low energy images. Our results demonstrate that the proposed NLM outperforms the conventional method using an anisotropic diffusion filter (ADF) and median techniques for all phantom and real human DXA images. CONCLUSIONS: Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

BACKGROUND AND OBJECTIVE: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. METHODS: The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. RESULTS: Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. CONCLUSIONS: Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions.

Loganin enhances long-term potentiation and recovers scopolamine-induced learning and memory impairments.

Although the incidence rate of dementia is rapidly growing in the aged population, therapeutic and preventive reagents are still suboptimal. Various model systems are used for the development of such reagents in which scopolamine is one of the favorable pharmacological tools widely applied. Loganin is a major iridoid glycoside obtained from Corni fructus (Cornusofficinalis et Zucc) and demonstrated to have anti-inflammatory, anti-tumor and osteoporosis prevention effects. It has also been found to attenuate Aß-induced inflammatory reactions and ameliorate memory deficits induced by scopolamine. However, there has been limited information available on how loganin affects learning and memory both electrophysiologically and behaviorally. To assess its effect on learning and memory, we investigated the influence of acute loganin administration on long-term potentiation (LTP) using organotypic cultured hippocampal tissues. In addition, we measured the effects of loganin on the behavior performance related to avoidance memory, short-term spatial navigation memory and long-term spatial learning and memory in the passive avoidance, Y-maze, and Morris water maze learning paradigms, respectively. Loganin dose-dependently increased the total activity of fEPSP after high frequency stimulation and attenuated scopolamine-induced blockade of fEPSP in the hippocampal CA1 area. In accordance with these findings, loganin behaviorally attenuated scopolamine-induced shortening of step-through latency in the passive avoidance test, reduced the percent alternation in the Y-maze, and increased memory retention in the Morris water maze test. These results indicate that loganin can effectively block cholinergic muscarinic receptor blockade -induced deterioration of LTP and memory related behavioral performance. Based on these findings, loganin may aid in the prevention and treatment of Alzheimer's disease and learning and memory-deficit disorders in the future.

The effect of tissue anisotropy on the radial and tangential components of the electric field in transcranial direct current stimulation.

Transcranial direct current stimulation (tDCS) is considered to be a promising technique for noninvasive brain stimulation and brain disease therapy. Recent studies have investigated the distribution of the electric field (EF) magnitude over gyri and sulci and the effect of tissue homogeneity with isotropic electrical conductivities. However, it is well known that the skull and white matter (WM) are highly anisotropic electrically, requiring investigations of their anisotropic effects on the magnitude and the directional components of the induced EF due to the high dependency between neuromodulation and the EF direction. In this study, we investigated the effects of the skull and WM anisotropy on the radial and tangential components of the EF via gyri-specific high-resolution finite element head models. For tDCS, three configurations were investigated: the conventional rectangular pad electrode, a 4(cathodes) +1(anode) ring configuration, and a bilateral configuration. The results showed that the skull anisotropy has a crucial influence on the distribution of the radial EF component. The affected cortical regions by the radial EF were reduced about 22 % when considering the skull anisotropy in comparison with the regions with the skull isotropy. On the other hand, the WM anisotropy strongly alters the EF directionality, especially within the sulci. The electric current tends to flow radially to the cortical surface with the WM anisotropy. This effect increases the affected cortical areas by the radial EF component within the sulcal regions. Our results suggest that one must examine the distribution of the EF components in tDCS, not just the magnitude of the EF alone.

An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier.

BACKGROUND: A typical P300-based spelling brain computer interface (BCI) system types a single character with a character presentation paradigm and a P300 classification system. Lately, a few attempts have been made to type a whole word with the help of a smart dictionary that suggests some candidate words with the input of a few initial characters. METHODS: In this paper, we propose a novel paradigm utilizing initial character typing with word suggestions and a novel P300 classifier to increase word typing speed and accuracy. The novel paradigm involves modifying the Text on 9 keys (T9) interface, which is similar to the keypad of a mobile phone used for text messaging. Users can type the initial characters using a 3×3 matrix interface and an integrated custom-built dictionary that suggests candidate words as the user types the initials. Then the user can select one of the given suggestions to complete word typing. We have adopted a random forest classifier, which significantly improves P300 classification accuracy by combining multiple decision trees. RESULTS AND DISCUSSION: We conducted experiments with 10 subjects using the proposed BCI system. Our proposed paradigms significantly reduced word typing time and made word typing more convenient by outputting complete words with only a few initial character inputs. The conventional spelling system required an average time of 3.47 min per word while typing 10 random words, whereas our proposed system took an average time of 1.67 min per word, a 51.87% improvement, for the same words under the same conditions.

Robust extraction of P300 using constrained ICA for BCI applications.

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.

The effects of acupuncture (PC6) on chronic mild stress-induced memory loss.

A previous study reported that the PC6 acupuncture point can alleviate chronic mild stress (CMS)-induced anxiety [17]. Following the previous study, this study examined the effects of the PC6 acupuncture point on CMS-induced memory loss. The memory storage and acetylcholinesterase (AchE) activity in the hippocampus were measured, respectively, using a passive avoidance test (PAT) and AchE immunohistochemistry. In the PAT (retention test), the CMS group showed a markedly lower latency time than the control (post (72h): P<0.01, post (96h): P<0.05, post (120h): P<0.001). However, acupuncture at PC6 significantly recovered the impairment of memory compared to the CMS group (post (120h): P<0.001). Exposure to CMS also significantly decreased the AchE activity in the hippocampus compared to the control rats. Acupuncture stimulation at the PC6 point on the pericardium channels (3min), but not at other points (TE5), produced memory improvements and an increase in AchE reactivity in the hippocampus compared to the CMS group. These results show that the acupuncture point is effective in restoring the CMS-related biochemical and behavioral impairments, such as learning and memory.

The neuroprotective effect of methanol extract of gagamjungjihwan and fructus euodiae on ischemia-induced neuronal and cognitive impairment in the rat.

Gagamjungjihwan (GJ), a decoction consisting of five herbs including ginseng, Acori Graminei Rhizoma, Uncariae Ramulus et Uncus, Polygalae Radic and Frustus Euodiae (FE), has been widely used as herbal treatment for ischemia. In order to investigate the neuroprotective action of this novel prescription, we examined the influence of GJ and FE on learning and memory using the Morris water maze and studied their affects on the central cholinergic system in the hippocampus with neuronal and cognitive impairment. After middle cerebral artery occlusion was applied for 2 h, rats were administered GJ (200 mg kg(-1), p.o.) or FE (200 mg kg(-1), p.o.) daily for 2 weeks, followed by training and performance of the Morris water maze tasks. Rats with ischemic insults showed impaired learning and memory of the tasks. Pre-treatment with GJ and FE produced improvement in the escape latency to find the platform. Pre-treatments with GJ and FE also reduced the loss of cholinergic immunoreactivity in the hippocampus. The results demonstrated that GJ and FE have a protective effect against ischemia-induced neuronal and cognitive impairment. Our results suggest that GJ and FE might be useful in the treatment of vascular dementia.

The Effects of Puerariae Flos on Stress-induced Deficits of Learning and Memory in Ovariectomized Female Rats.

Puerariae flos (PF) is a traditional oriental medicinal plant and has clinically been prescribed for a long time. The purpose of the present study was to examine the effect of PF on repeated stress-induced alterations of learning and memory on a Morris water maze (MWM) test in ovariectomized (OVX) female rats. The changes in the reactivity of the cholinergic system were assessed by measuring the immunoreactive neurons of choline acetyltransferase (ChAT) in the hippocampus after behavioral testing. The female rats were randomly divided into four groups: the nonoperated and nonstressed group (normal), the sham-operated and stressed group (control), the ovariectomized and stressed group (OS), and the ovariectomized, stressed and PF treated group (OSF). Rats were exposed to immobilization stress (IMO) for 14 d (2 h/d), and PF (400 mg/kg, p.o.) was administered 30 min before IMO stress. Results showed that treatments with PF caused significant reversals of the stress-induced deficits in learning and memory on a spatial memory task, and also increased the ChAT immunoreactivities. In conclusion, administration of PF improved spatial learning and memory in OVX rats, and PF may be useful for the treatment of postmenopausal-related dementia.

The effects of acupuncture stimulation at PC6 (Neiguan) on chronic mild stress-induced biochemical and behavioral responses.

In the present study, the effects of acupuncture on the behavioral and physiological responses induced by chronic mild stress (CMS) were evaluated. Sprague-Dawley rats were exposed to a variety of chronic unpredictable, mild stressors for 8 weeks. The effects of acupuncture on stress-induced anxiety and anhedonia were investigated using the elevated plus maze (EPM) and sucrose intake test. In addition, c-fos expression, as an early neuronal marker in the brain was also examined utilizing Fos-like immunohistochemistry (FLI). CMS rats significantly reduced the consumption of sucrose intake and latency in the open arms of the EPM, and gained body weight more slowly, compared to non-stressed normal rats. Exposure to CMS also significantly increased FLI in the paraventricular nucleus (PVN) of the hypothalamus. Acupuncture stimulation at point PC6 on the pericardium channels (3 min), but not at other point (TE5), restored stress-induced decrease in the latency in the open arms and significantly attenuated FLI in the PVN produced by CMS. Acupuncture stimulation also tended to restore stress-induced decrease in the sucrose intake. The present results demonstrated that acupuncture was effective in restoring CMS-related biochemical and behavioral impairments such as anxiety and anhedonia and that acupuncture point was more effective than non-acupuncture point. These results suggest that acupuncture has a therapeutic effect on chronic stress-related diseases such as depression and anxiety.

Nicotine- and methamphetamine-induced dopamine release evaluated with in-vivo binding of radiolabelled raclopride to dopamine D2 receptors: comparison with in-vivo microdialysis data.

The effect of substances which alter extracellular dopamine (DA) concentration has been studied by measuring changes in the binding of radiolabelled raclopride, a DA D2 receptor ligand that is sensitive to endogenous DA. To better characterize the relationship between extracellular DA concentration and DA D2 receptor binding of raclopride, we compared the changes of extracellular DA concentration (measured using in-vivo microdialysis) and in-vivo [3H]raclopride binding induced by different doses of methamphetamine (Meth) and nicotine, drugs that enhance DA release with and without blocking DA transporters (DATs), respectively, in rat striatum. Nicotine elicited a modest increase of striatal extrasynaptic extracellular DA, while Meth produced a marked increase of striatal extrasynaptic DA in a dose-dependent manner. There was a close correlation between the decrease in [3H]raclopride in-vivo binding and the increase in extrasynaptic DA concentration induced by both nicotine (r2=0.95, p<0.001) and Meth (r2=0.98, p=0.001), supporting the usefulness of the radiolabelled raclopride-binding measurement for the non-invasive assessment of DA release following interventions in the living brain. However, the linear regression analysis revealed that the ratio of percent DA increase to percent [3H]raclopride binding reduction was 25-fold higher for Meth (34.8:1) than for nicotine (1.4:1). The apparent discrepancy in the extrasynaptic DA-[3H]raclopride binding relationship between the DA-enhancing drugs with and without DAT-blocking property indicates that the competition between endogenous DA and radiolabelled raclopride takes place at the intrasynaptic rather than extrasynaptic DA D2 receptors and reflects synaptic concentration of DA.

Acupuncture attenuates cocaine-induced expression of behavioral sensitization in rats: possible involvement of the dopaminergic system in the ventral tegmental area.

Acupuncture is widely used for the treatment of many functional disorders, such as substance abuse, and has the suppressive effect on the central nervous system. Many studies have suggested that behavioral sensitization by repeated injections of cocaine produce an increase in locomotor activity and an increase in the expression of tyrosine hydroxylase (TH), in the central dopaminergic system. In order to investigate the effects of acupuncture on the repeated cocaine-induced neuronal and behavioral sensitization alternations, we examined the influence of acupuncture on the repeated cocaine-induced locomotor activity and the expression of TH in the brain using immunohistochemistry. Male SD rats were given repeated injections of cocaine hydrochloride (15 mg/kg, i.p. for 10 consecutive days) followed by one challenge injection on the 4th day after the last daily injection. Cocaine challenge produced a large increase in the locomotor activity and the expression of TH in the ventral tegmental area (VTA). Treatment with acupuncture bilaterally at the Shenman (HT7) points for 1 min significantly inhibited the increase of locomotor activity as well as the TH expression in the VTA. Our data demonstrated that the inhibitory effects of acupuncture on cocaine-induced expression of behavioral sensitization were closely associated with the reduction of dopamine (DA) biosynthesis and the postsynaptic neuronal activity. These results provide evidence that acupuncture may be effective for inhibiting the behavioral effects of cocaine by possible modulation of the central dopaminergic system.

Comparison of the antiobesity effects of the protopanaxadiol- and protopanaxatriol-type saponins of red ginseng.

A previous study demonstrated that ginseng crude saponins prevent obesity induced by a high-fat diet in rats. Ginseng crude saponins are known to contain a variety of bioactive saponins. The present study investigated and compared the antiobesity activity of protopanaxadiol (PD) and protopanaxatriol (PT) type saponins, major active compounds isolated from crude saponins. Male 4-week-old Sprague-Dawley rats were fed with normal diet (N) or high-fat diet (HF). After 5 weeks, the HF diet group was subdivided into the control HF diet, HF diet-PD and HF diet-PT group (50 mg/kg/day, 3 weeks, i.p.). Treatment with PD and PT in the HF diet group reduced the body weight, total food intake, fat contents, serum total cholesterol and leptin to levels equal to or below the N diet group. The hypothalamic expression of orexigenic neuropeptide Y was significantly decreased with PD or PT treatment, whereas that of anorexigenic cholecystokinin was increased, compared with the control HF diet group. In addition, PD type saponins had more potent antiobesity properties than PT saponins, indicating that PD-type saponins are the major components contributing to the antiobesity activities of ginseng crude saponins. The results suggest that the antiobesity activity of PD and PT type saponins may result from inhibiting energy gain, normalizing hypothalamic neuropeptides and serum biochemicals related to the control of obesity.

Inhibitory effects of ginseng total saponins on behavioral sensitization and dopamine release induced by cocaine.

Many studies have suggested that the behavioral and reinforcing effects of cocaine can be mediated by the central dopaminergic systems. It has been shown that repeated injections of cocaine produce an increase in locomotor activity, the expression of the immediate-early gene, c-fos, and the release of dopamine (DA) in the nucleus accumbens (NAc), which is one of the main dopaminergic terminal areas. Several studies have shown that behavioral activation and changes in extracellular dopamine levels in the central nervous system induced by psychomotor stimulants are prevented by ginseng total saponins (GTS). In order to investigate the effects of GTS on the repeated cocaine-induced behavioral and neurochemical alterations, we examined the influence of GTS on the cocaine-induced behavioral sensitization and on c-Fos expression in the brain using immunohistochemistry in rats repeatedly treated with cocaine. We also examined the effect of GTS on cocaine-induced dopamine release in the NAc of freely moving rats repeatedly treated with cocaine using an in vivo microdialysis technique. Pretreatment with GTS (100, 200, 400 mg/kg, i.p.) 30 min before the daily injections of cocaine (15 mg/kg, i.p.) significantly inhibited the repeated cocaine-induced increase in locomotor activity as well as the c-Fos expression in the core and shell in a dose-dependent manner. Also, pretreatment with GTS significantly decreased the repeated cocaine-induced increase in dopamine release in the NAc. Our data demonstrate that the inhibitory effects of GTS on the repeated cocaine-induced behavioral sensitization were closely associated with the reduction of dopamine release and the postsynaptic neuronal activity. The results of the present study suggest that GTS may be effective for inhibiting the behavioral effects of cocaine by possibly modulating the central dopaminergic system. These results also suggest that GTS may prove to be a useful therapeutic agent for cocaine addiction.