2222 results about "Mammary gland structure" patented technology

A small percentage of cells within an established solid tumor have the properties of stem cells. These solid tumor stem cells give rise both to more tumor stem cells and to the majority of cells in the tumor that have lost the capacity for extensive proliferation and the ability to give rise to new tumors. Thus, solid tumor heterogeneity reflects the presence of tumor cell progeny arising from a solid tumor stem cell. We have developed a xenograft model in which we have been able to establish tumors from primary tumors via injection of tumor cells in the mammary gland of severely immunodeficient mice. These xenograft assay have allowed us to do biological and molecular assays to characterize clonogenic solid tumor stem cells. We have also developed evidence that strongly implicates the Notch pathway, especially Notch 4, as playing a central pathway in carcinogenesis.

The invention provides a method for detecting an X-ray mammary gland lesion image based on feature pyramid network under transfer learning. The method comprises the steps: 1, establishing a source domain data set and a target domain data set; 2, establishing a deformable convolution residual network layer by a deformable convolution and extended residual network module; 3, establishing a multi-scale feature extraction sub-network based on a feature pyramid structure through a feature map up-sampling and feature fusion method in combination with a deformable convolution residual network layer;4, establishing a deformable pooling sub-network sensitive to the focus position; 5, establishing a post-processing network layer to optimize a prediction result and a loss function; and 6, migratingthe training model to a small sample molybdenum target X-ray mammary gland focus detection task so as to improve the detection precision of the network model on the focus in the small sample image. According to the method, a transfer learning strategy is combined to realize focus image processing in a small sample medical image.

A composition comprising a mixture of fatty acids such as, linoleic, oleic and palmitic acids or deriviatives thereof dervived from secretions o mammalian mammary glands. The compositions can be utilized to decrease stress, anxiety and aggressiveness in mammals.

The invention discloses an image segmentation method for a dynamic contrast-enhanced mammary gland MRI sequence, pertaining to the field of magnetic resonance image processing techniques, which is characterized by comprising the following steps: a three-dimensional magnetic resonance image sequence of the section of the mammary gland is put into a computer; the image is divided into two parts including a mammary gland-air interface and a mammary gland-chest interface; a breast-air boundary is obtained by a splitting transaction in which a dynamic threshold controls the regional growth; an initial profile of the mammary gland and the chest is obtained in the same way, the complex profile of the breast and the chest is obtained with a method of controlling a level set; a three-dimensional magnetic resonance image sequence of a point-in-time is obtained by split jointing the segmentation results and taken as an initial position of the next group three-dimensional image segmentation. The image segmentation method of the invention increases the segmentation speed, solves the problem that a level set algorithm can not easily determine the initial profile and the velocity function and realizes an automatic segmentation of the complex dynamic contrast-enhanced mammary-gland magnetic resonance image with plenty of data.

The invention belongs to the field of feeds, and particularly relates to a six percent-premix for lactating sows and a wheat-type daily ration prepared from the six percent-premix. The premix comprises multivitamins, composite trace elements, DL-methionine, lysine, threonine, L-arginine, valine, composite electrolyte, compound enzyme, 5000 IU of phytase, functional additives, feed attractants, calcium hydrogen phosphate, stone powder, salt, choline, antioxidants and defatted rice bran. On the whole, the daily ration for the lactating sows is capable of protecting the development of mammary glands of the sows, ensuring plentiful milk with high quality in the lactation period of the sows and increasing the survival rate of suckling piglets, and enables the piglets to have higher weaning weight.

The invention provides traditional Chinese medicine for treating breast diseases, which is formed by mixing Chinese angelica, red paeony root, peach kernels, red flower, salvia miltiorrhiza, zedoary turmeric oil, rhizoma corydalis, szechuan lovage rhizome, lumbricus, pangolin scales, uniflower swisscentaury root, fritillaria thunbergii, spina gleditsiae, cowherb seeds, angelica root, mongolian snakegourd root, concha ostreae, pericarpium citri reticulatae viride, nutgrass galingale rhizome, tangerine seeds, radix bupleuri, dandelion, rhizoma pleionis, fructus forsythiae, astragalus root, codonopsis pilosula, liquorice, frankincense, myrrh, poria cocos and oldenlandia diffusa. The traditional Chinese medicine has the effects of invigorating Qi and enriching the blood, invigorating the blood circulation and softening the hard lumps, activating the meridians and detoxicating, soothing the depressed livers and regulating the flow of Qi, softening and resolving the hard lumps, invigorating the spleens and reducing the phlegm, strengthening the healthy energy and eliminating the pathogenic factors and can be used for treating hyperplasia of mammary gland, acute or chronic mastitis, breast fibroadenoma, intraductal papilloma, breast lump, breast scrofula, breast carbuncle and breast cancer.

The invention relates to an interactive film interpretation method of a mammary gland CR image. At present, an artificial film interpretation method has low efficiency and accuracy. The method comprises: firstly, reading a mammary gland CR image to be processed; secondly, selecting a region of suspected lesion and computing a scanning line cross section histogram, a distance between two points, a gray scale histogram, a grey scale standard deviation, an area, a circumference, a compactness, an aspect ratio, a distance from edge to center and a fractal dimension; thirdly, performing visual enhancement processing of the region of the suspected lesion by using an image enhancement technique; and finally, searching a lesion having a similar appearance in a lesion database to provide a reference for tumor diagnosis. The method of the invention can conveniently and quickly perform local observation, detailed enhancement and parameter measurement of the mammary gland CR image, improve film interpretation efficiency, reduce working load and improve precision.

The invention discloses a deep residual network-based semantic mammary gland molybdenum target image lump segmentation method. The method comprises the following steps of: labelling pixel categories of lumps and normal tissues corresponding to a collected mammary gland molybdenum target image so as to generate label images, and dividing the mammary gland molybdenum target image and the corresponding label images into training samples and test samples; preprocessing the training samples to form a training data set; constructing a deep residual network, and training the network by utilizing thetraining data set, so as to obtain a deep residual network training model; after a to-be-segmented mammary gland molybdenum target image lump is preprocessed, carrying out binary classification and post-processing on a pixel of the to-be-segmented mammary gland molybdenum target image by utilizing the deep residual network training model, and outputting lump segmentation image to realize semanticsegmentation of the mammary gland molybdenum target image lump. The method is capable of effectively improving the automatic and intelligent levels of mammary gland molybdenum target image lump segmentation, and can be applied to the technical field of assisting radiologists to carry out medical diagnosis.

The invention discloses an external plaster for treating hyperplasia of mammary glands, which is prepared from raw materials of bupleurum root, radix paeoniae alba, radix curcumae, rhizoma cyperi, green tangerine orange peel, dried tangerine peel, ginseng, tuckahoe, rhizoma atractylodis alba, semen coicis, raw licorice, Chinese angelica, radix astragali, chicken gizzardskin, brassica alba boiss, scrophulariaceae, sesame oil, camphor powder and the like according to a traditional method for decocting a black plaster. The external plaster mixes the medicaments into a plaster and is applied to niduses or relative acupuncture points to play a role of smoothing the liver, invigorating the spleen, tonifying the kidney and dispersing accumulation of pathogens. The external plaster achieves the aims of promoting qi and blood, regulating yin and yang, warming kidney to invigorate yang, discharging liver and regulating qi, promoting blood circulation by removing blood stasis, softening hard lumps and dispelling nodes, and regulating thoroughfare and conception vessels.

The invention provides a mammary gland lesion area detection method based on deep learning and transfer learning. The method comprises: preparation and amplification of a training set and a test set;according to lump position information marked by a doctor in the breast data set, extracting an available lump image and normalizing the size of the available lump image into a size of 100 * 100 pixels as a positive sample; according to the invention, the AlexNet network is used to train the parameter model of the classification model of the natural image on the ImageNet data set; training and transfer learning are carried out on a specific breast image data set, so that the binary classification problem of the convolutional neural network on a small-scale breast data set can be successfully solved, a lesion area in the breast image can be identified, and the prediction effect on the breast lesion is improved.

The invention discloses a dandelion health care tea which consists of the following components and mass percentage of the components is: dandelion leaves: 50 to 70 percent; dandelion rootlets: 25 to 35 percent; ganoderma lucidum rootlets: 2 to 8 percent; and wild chrysanthemum: 2 to 8 percent. The invention also discloses a preparation method of the health care tea which comprises the following steps: first cleaning the dandelion, and cutting the leaves and the root off by a knife, dipping the leaves in warm water for 3 minutes and drying after cutting the leaves into lumps of 1 square centimeter; cutting the root into threads by a blanket machine and drying; cutting the ganoderma lucidum into threads by the blanket machine and drying, and drying the wild chrysanthemum directly, then the health care tea is obtained by mixing and sterilizing the raw materials according to the mass percentage. The health care tea of the invention can effectively prevent and treat hyperplasia of mammary glands at the same time when satisfying the needs of drinking tea. After drinking the health care tea, the occurrence of mammary gland diseases such as hyperplasia of mammary glands of the female can be prevented; the female who suffer from the mammary gland diseases such as hyperplasia of mammary glands can also have good treatment effect after drinking the health care tea without side effects, risk of operation or scar.

The present invention relates to efflux inhibitor compositions and methods of using these agents for treating conditions where the activity of efflux transporter proteins (e.g., Breast Cancer Resistance Protein (BCRP) and P-Glycoprotein (P-GP)) inhibit effective delivery of a therapeutic agent to a target tissue (e.g., brain, spinal cord, nerves, cerebrospinal fluid, testis, eyeballs, retina, inner ear, placenta, mammary gland, liver, biliary tract, kidney, intestines, lung, adrenal cortex, endometrium, hematopoietic cells, and / or stem cells).

In a medical imaging apparatus for imaging mammary gland and breast while compressing the breast with a compression plate, the posture of an ultrasonic probe relative to the compression plate is kept constant and the moving motion of the ultrasonic probe is stabilized. The apparatus includes: an imaging stage; a compression plate; an ultrasonic probe provided to maintain acoustic connection to the compression plate, for transmitting ultrasonic waves according to drive signals and receiving ultrasonic echoes to output reception signals; an ultrasonic imaging unit for supplying the drive signals to the ultrasonic probe and generating image data based on the reception signals; a detecting unit for detecting a location and / or a posture of the ultrasonic probe relative to the compression plate; and a control unit for controlling the location and / or the posture of the ultrasonic probe based on a detection result of the detecting unit.

The invention discloses a mammary gland molybdenum target image automatic classification method based on deep learning. The method comprises the following steps that step one, square image blocks are selected from the cancerous area and the normal area of a mammary gland molybdenum target image by using different sizes of sliding windows, and a training sample set and a test sample set corresponding to each size are constructed for different sizes of image blocks; step two, a convolutional neural network model corresponding to each size is established, and the model is trained by using the training sample set for each size; step three, the accuracy rate of the corresponding convolutional neural network model is tested by using the test sample set for each size, and the convolutional neural network model for the size corresponding to the highest accuracy rate is selected; step four, the overall connection layer characteristics are extracted by using the selected convolutional neural network model; and step five, the extracted characteristics are inputted to a linear SVM classifier for classification so that the classification types of the image blocks are obtained. The overall connection layer characteristics in the convolutional neural network model are extracted to act as the classification characteristics of the image blocks so that the classification speed and accuracy can be enhanced.

The invention discloses a Chinese medicinal preparation for treating breast diseases and uterine fibroid and a preparation method thereof. The Chinese medicinal preparation is prepared by extracting and mixing the following raw materials in percentage by weight: 8 to 15 percent of Chinese thorowax root, 4 to 6 percent of turmeric root-tuber, 3 to 6 percent of pangolin, 4 to 6 percent of nutgrass galingale rhizome, 3 to 5 percent of thunbery fritillary bulb, 3 to 5 percent of snake gourd fruit, 3 to 4 percent of hemlock parsley, 3 to 5 percent of suberect spatholobus stem, 2 to 4 percent of red-rooted salvia root, 2 to 3 percent of common burreed rhizome, 2 to 3 percent of curcuma zedoary, 2 to 3 percent of frankincense, 2 to 3 percent of myrrh, 3 to 5 percent of rhizoma corydalis, 3 to 5 percent of turtle shell, 2 to 4 percent of spina gleditsiae, 2 to 3 percent of semen litchi, 5 to 6 percent of selfheal, 3 to 6 percent of Astragalus root, 2 to 4 percent of angelica, 2 to 3 percent of Atractylis ovate, 2 to 3 percent of white peony root, 2 to 3 percent of poria, and 2 to 3 percent liquorice. The Chinese medicinal preparation can be used for treating patients with hyperplasia of mammary glands, mamstitis or hysteromyoma, and has high clinical cure rate and good healing effect.

The invention provides a method and a system for automatically analyzing a mammary gland ultrasonic image, identifying a mammary gland focus and then carrying out focus area contour extraction and parameter calculation. According to the invention, the convolutional neural network is used to construct the recognition classification recognition and segmentation extraction model, the breast lesion area in the video or image can be recognized, the lesion area contour can be automatically extracted, and the parameters of the lesion area can be calculated. According to the method and the system, theauxiliary diagnosis accuracy is remarkably improved, repeated operations of doctors are greatly reduced, and the robustness is good.

A transgenic, non-human mammalian animal is capable of expressing a heterologous gene for human or other recombinant physiologically functional fibrinogen holoprotein or individual subunit chain polypeptides thereof or a modified or fusion fibrinogen in mammary glands of the animals and secreting the expressed product into a body fluid. Methodology employing such a mammal yields recombinant physiologically functional fibrinogens, subunit chain polypeptides thereof, and modified or fusion fibrinogens.

The invention relates to the field of livestock breeding technology, which specifically relates to a forage for sow during lactation period, and the forage is prepared by the following raw materials by weight: 70-80 parts of corn, 20-25 parts of bean cakes, 20-25 parts of millet bran, 15-18 parts of wheat bran, 12-15 parts of pea residues, 10-12 parts of dried white radish, 5-8 parts of black sesame, 3-4 parts of pig heat powder, 6-8 parts of vegetable sponge of luffa, 4-5 parts of fish meals, 2-3 parts of stone flour, 1-2 parts of shell powder, 5-6 parts of laver powder, 8-10 parts of purslane, 1-2 parts of radix Rhapontici, 2-3 parts of cape jasmine, 1-2 parts of pitch, 1-2 parts of Akebia stems, a proper amount of salts and 4-5 parts of feeding promoting agents. The prepared sow forage has balanced and comprehensive nutrition, and can satisfy each nutrition demand of postpartum sow, radix Rhapontici, cape jasmine, pitch, Akebia stems and other Chinese herbal medicine components can free mammary gland, increase milk yield, and the pigling fed by the sow which eats the forage has high survival rate, strong physique and red and bright hair.

The invention relates to a computer-aided mammy gland diagnosing method by means of characteristic weight adaptive selection. The method comprises the steps of firstly extracting a mammary gland X-ray molybdenum target and a type-B ultrasonic image data characteristic, performing benign-malignant and clinic periodic marking on case data after characteristic extraction according to known clinical diagnosis results; performing multi-characteristic fusion on the X-ray molybdenum target image and the type-B ultrasonic image of the mammary gland of a same patient according to a cascaded manner, and obtaining the characteristic vector of a mammary gland sample; afterwards using a characteristic weight adaptive selection method on the training process of a binary balanced decision tree SVM multi-class classification algorithm based on a Gaussian kernel; and finally utilizing the characteristic weight adaptive selection method on the identification process of the binary balanced decision tree SVM multi-class classification algorithm based on the Gaussian kernel. The computer-aided mammy gland diagnosing method can improve accuracy and efficiency in breast cancer diagnosis.

The present invention provides materials and methods for the production of recombinant BMPs in transgenic animals. In particular, the invention provides materials and methods for the production of recombinant BMPs in the milk of transgenic animals that express recombinant BMPs in the mammary gland.

A radiographing apparatus of an image radiographing system is equipped with plural sensors each detecting an amount of radiation transmitted through a breast in the course of radiographing, and It transmits sensor data showing an output value of each sensor to a control apparatus through a network. In the control apparatus, a mammary gland content rate corresponding to an output value of each sensor is acquired based on sensor data transmitted from the radiographing apparatus, then, a breast type is discriminated based on an average mammary gland content rate and on the state of distribution of mammary gland content rates, and image processing conditions are established based on the results of discrimination of the breast type. Then, based on the image processing conditions thus established, image processing is given to image data acquired from a reading apparatus.

A tetrahydroindolone derivative and its use are disclosed. It can inhibit malignant tumor cell growth. It can be used for mammary gland cancer, lung cancer, uterus neck cancer, rectum cancer, liver cancer and blood cancer.

A mammary gland content rate estimating apparatus, includes: a breast image acquiring device which acquires a breast image obtained by radiographing a breast by a mammography imaging apparatus; an adipose image estimating device which estimates an adipose image from the acquired breast image based on an assumption that an entire breast is composed of only adipose tissues; a device which acquires a pixel value of a directly irradiated region from the acquired breast image; and a mammary gland content rate calculating device which calculates a mammary gland content rate for each of pixels in the breast image based on the acquired breast image, the estimated adipose image and the acquired pixel value of the directly irradiated region. Accordingly, without requiring complicated calibration in advance, the mammary gland content rate can be estimated for each of pixels easily and precisely.

The invention discloses a novel double-mode mammary gland three-dimensional imaging device and method. The device comprises a main machine body, a rack, a scanning platform, a measuring arm, an ultrasonic imaging unit, an infrared imaging unit, a control module, an infrared imaging device which is mounted in the measuring arm and a probe scanning mechanism which is mounted in the scanning platform, an ultrasonic probe and an infrared light emitting device are disposed on the probe scanning mechanism, and the scanning mechanism is opposite to the infrared imaging device. By means of the double-mode mammary gland three-dimensional imaging device and method, collection of ultrasonic image signals and infrared image signals can be performed simultaneously during one-time scanning, the ultrasonic images are related to the infrared images in space, breast three-dimensional volume ultrasonic images and infrared images can be re-established, overlay analysis of double-mode images can be achieved, check and diagnosis for breast tissue can be facilitated, and noninvasive undamaged and non-radiative effects are achieved.

A method and pharmaceutical composition for ceasing milk production, for inducing involution, or for treating infection in a mammary gland of a lactating animal is described. The method is effected by direct administration of calcium chelators to the gland, or upon administration of enzymes which cause production of chelators in situ. The invention can be used to change the physiologic state of a single mammary gland of a lactating animal without significantly affecting the physiologic state of other mammary glands of the same animal. Changes resulting from use of the invention may be either transient or long lasting. The invention is expected to have uses in commercial agriculture and human medicine.

The invention particularly discloses a composition for promoting opening of skin aquaporin, which is mainly proportionally prepared from deionized water, glycerol, carbomer, xanthan gum, lycine, trehalose, inositol, taurine, lauryl mammary gland sodium lactate, monomethyl-monosilane triol, oxyproline, aspartic acid, yeast amino acid, polyglutamic acid, sodium hyaluronate, dipotassium glycyrrhetate, purslane extracting solution, licorice extract, ceramide, phytosphingosine, cholesterol, oligopeptide-1, polyquaternary amine salt-14 and the like. The composition has the function of promoting opening of skin aquaporin, has better water replenishing and locking effect, and effectively prevents and resists the phenomena of dry skin mucosa and skin aging.

The invention relates to the field of medical image processing and pattern recognition, and provides a computer-assisted lump detecting method based on a mammary gland magnetic resonance image. The computer-assisted lump detecting method based on the mammary gland magnetic resonance image aims at solving the problems that in the prior art, the lump partition effect is not good, and the accuracy, the sensitivity and the specificity in a classification test are not high. The computer-assisted lump detecting method includes the following steps: S1, extracting an interest area from the mammary gland magnetic resonance image; S2, extracting an initial lump area from the interest area in a separated mode, and determining the contour line of the initial lump area; S3, calculating the weight distribution of characteristic parameters of the initial lump area; S4, selecting the characteristic parameters, with the weight coefficients larger than a standard weight coefficient, of the initial lump area, and carrying out training classifying to obtain optimized characteristic parameters; S5, inputting the optimized characteristic parameters into a classifier, analyzing the optimized characteristic parameters with a support vector machine classification method, determining a final lump area, and displaying the final lump area for a user. The detecting method has the good lump partition effect, the accuracy, the sensitivity and the specificity in the classification test are effectively improved, the detecting result serves as a second opinion to be provided for a radiologist, and the misdiagnosis rate and the missed diagnosis rate of the radiologist can be effectively reduced.

A composition information obtaining unit calculates a mammary gland / fat ratio and a first information obtaining unit obtains imaged contrast information representing a contrast of the radiation image. A second information obtaining unit sets target application condition of X-ray, and obtains target contrast information representing an intended contrast for the radiation image based on the intended application condition. A contrast correction amount determination unit determines a contrast correction amount based on the imaged contrast information and the target contrast information. An image processing unit performs image processing, including gradation processing based on the determined contrast correction amount, on the radiation image, and obtains a processed radiation image.