45 results about "Brain tumor" patented technology

The application relates to a brain tumor segmentation method and device based on TriSAM-UNet, which comprises the following steps: S1, collecting a multi-modal MRI brain tumor image dataset, and performing pretreatment and data enhancement; S2, constructing a hybrid segmentation network TriSAM-UNet, designing a hybrid module SAHM, capturing three-dimensional dependence and cross-region jump correlation, and realizing all-around global context perception; S3, constructing an FUE module, inhibiting fuzzy and noise response to ensure that high-confidence features are preferentially transmitted; S4, constructing a WFAB module, and restoring the boundary and texture details lost in up-sampling; and S5, training and optimizing the network model and performing full-automatic segmentation on the brain tumor image. The application aims to solve the problems of insufficient global context capture, indiscriminate forwarding of noise features in jump connection and loss of boundary details in up-sampling in brain tumor segmentation, and is suitable for the field of brain tumor image analysis.

The application discloses a 2.5D MRI brain tumor segmentation method based on Mamba state space modeling and belongs to the technical field of medical image segmentation. The application aims at the problems of the existing brain tumor segmentation method, such as long-range dependence modeling deficiency, high calculation cost, limited boundary segmentation precision and poor adaptability of the Mamba architecture to the 2.5D segmentation scene, and a cross-slice priority traversal module suitable for the 2.5D slice space structure is designed, local details and global semantic features are balanced through a feature calibration and controlled fusion module, and a global perception and edge enhancement dual-decoder structure is constructed to decouple tumor global modeling and boundary modeling tasks. The application reduces the calculation complexity while ensuring the segmentation precision, solves the problems of tumor segmentation discontinuity and boundary blur and is suitable for clinical conventional image workstations and has a good application prospect.

This invention belongs to the field of medical image processing technology, specifically relating to a brain tumor segmentation method based on a dynamically enhanced diffusion model. The core design incorporates a dynamically convolutional kernel-driven multi-scale feature adaptive fusion mechanism implemented in a lightweight dynamic attention denoising UNet network (LDA-DU). Simultaneously, a lightweight global enhancement feature encoder LG-FE is designed to provide a high-quality multi-scale feature base for this core mechanism, and a step-size uncertainty fusion module adaptively fuses the multi-step outputs of this core mechanism. Through this end-to-end architecture of "core mechanism + supporting modules," accurate 3D brain tumor segmentation (WT / TC / ET) is achieved. This simultaneously addresses the imbalance between lightweight design and accuracy, and the low efficiency of multi-step prediction, improving the model's robustness to non-ideal clinical data and providing technical support for rapid and accurate clinical diagnosis of brain tumors.

This application provides a MedSAM-based dual-stream adaptive brain tumor segmentation method and apparatus, belonging to the field of image segmentation processing technology. By introducing a 2.5D dual-stream image encoder and a low-rank adaptive fine-tuning mechanism, it effectively alleviates the enormous computational overhead in 3D medical image feature extraction, while overcoming the defect of lack of interlayer continuity in traditional 2D single-slice methods. The method designs a lightweight 3D convolution and residual mechanism in the intermediate layer of the encoder for cross-slice information interaction, explicitly separating features into a clean stream for generating self-hints and a fusion stream for providing spatially perceptual features. In the mask decoding stage, a residual soft modulation mechanism based on pixel-level confidence maps is introduced to weakly intervene in the decoded features, avoiding damage to the feature distribution of fine-grained tumor subregions and improving the segmentation accuracy of the basic large model in multimodal 3D medical images.

This invention discloses a method and system for identifying brain tumors in brain MRI images. The method includes the following steps: S1, acquiring the original brain MRI image data of the object to be identified and performing preprocessing operations to obtain standardized multimodal MRI image data; S2, performing preliminary tumor region screening on the preprocessed image data using a tumor tissue probability decoupling identification method based on multi-echo relaxation differences. This invention relates to the field of medical image processing technology. This method and system for identifying brain tumors in brain MRI images employs a tumor tissue probability decoupling model based on multi-echo relaxation differences and cross-scale brain network topology perturbation analysis, which can accurately screen potential tumor regions. Furthermore, it improves identification accuracy through dual constraints of metabolism and morphology. The introduction of cross-scale topology analysis, combined with abnormal propagation detection of brain network structures, effectively identifies suspected tumor regions and improves identification accuracy.

The application provides a traditional Chinese medicine composition for treating brain tumors and a preparation method and application thereof, and the traditional Chinese medicine composition comprises the following raw materials in parts by weight: 3-5 parts of tianlong, 3-5 parts of centipede, 6-12 parts of sanleng, 6-12 parts of zedoary, 15-60 parts of belamcanda chinensis, 9-12 parts of acorus gramineus, 10-20 parts of dandelion, 9-12 parts of poria cocos and 9-12 parts of atractylodes. The composition is prepared through the strict combination of monarch, minister, assistant and messenger and the synergistic effect of specific medicinal ingredients, nine drugs are combined, warm and cool are in order, and elimination and tonification are combined and cancelled, so that the effects of removing blood stasis and resolving masses, dispelling wind and calming are realized; the unique combination mechanism effectively restrains the toxic and side effects of insect drugs while enhancing the treatment intensity of removing blood stasis and resolving masses, and ensures the safety of the medicine; in addition, based on the syndrome differentiation strategy of flexible adjustment of the amount of monarch drug, the composition can be widely used for various types of brain tumor patients, and significant progress is achieved in the aspects of efficacy, safety and clinical applicability.

This invention belongs to the field of image classification technology and discloses a method and apparatus for classifying brain tumor magnetic resonance images. The invention acquires a dataset of brain tumor magnetic resonance images; constructs a classification model integrating a channel attention mechanism, a multi-scale convolutional neural network, and a support vector machine; wherein the channel attention mechanism is embedded in the feature extraction module of the multi-scale convolutional neural network, and the support vector machine acts as a classifier connected to the feature output end of the multi-scale convolutional neural network; the constructed classification model is trained using the dataset to obtain a trained classification model; the brain tumor magnetic resonance image to be classified is input into the trained classification model, and the classification result is output. This invention can improve the accuracy of brain tumor magnetic resonance image classification.

The application discloses a kind of low-quality MRI-oriented unsupervised cross-domain brain tumor segmentation methods.It includes CDGM-Net, the CDGM-Net includes three collaborative components: initial mask generation module IMG, frequency decomposition matching module FDM and frequency cross fusion module FCM;Specifically comprising the following steps: step S1.MRI image input and feature extraction;Step S2. Generate initial mask, obtain the initial foreground mask of query image feature;Step S3. Frequency decomposition, i.e. the first stage of FDM module;Step S4. Frequency distribution matching, i.e. the second stage of FDM module, obtain the low-frequency feature of aligned query image and aligned query high-frequency image feature;Step S5. Frequency cross fusion, i.e. FCM module: generate final brain tumor segmentation mask.The advantage is: in low-quality MRI scene, noise interference can be more effectively suppressed and the structure consistency of tumor region is maintained, the effectiveness and practical value of frequency domain alignment and cross-frequency fusion strategy in cross-dataset brain tumor segmentation task are verified.

This invention discloses a wasp venom peptide-M nanocomposite, its preparation method, and its applications, belonging to the field of medicinal chemistry. Using indocyanine green as a morphology mediator, this invention prepares an ultra-small particle size dendritic tetrasulfide by reacting it with triethylamine, hexadecyltrimethylammonium bromide, tetraethyl orthosilicate, and bis[3-(triethoxysilyl)propyl]tetrasulfide in water. Wasp venom peptide-M is then loaded onto this nanocomposite to obtain the wasp venom peptide-M nanocomposite. In vitro experiments have confirmed that this composite can significantly kill 4T1 breast cancer cells. Furthermore, thanks to its ultra-small particle size, it also exhibits good targeting and enrichment, significant anti-tumor efficacy, and excellent safety in a breast cancer brain metastasis model, providing a novel and efficient nanomedicine delivery system for brain tumor treatment and the development of related therapeutic drugs.

This invention belongs to the field of pharmaceutical formulation technology, specifically disclosing a gastrodin transdermal patch and its preparation method. The patch consists of a backing layer, a drug-containing matrix layer, and an anti-adhesive layer, using gastrodin as the active ingredient. It employs HP-β-CD inclusion technology, combined with an HPMC and PVP polymer backbone and an oleic acid-menthol composite transdermal enhancement system. The preparation utilizes solvent casting and low-temperature vacuum drying processes, which are mild and controllable. This invention effectively solves the problems of difficult transdermal absorption, easy recrystallization, and low bioavailability of gastrodin. The resulting patch has excellent adhesion, no skin irritation, high transdermal penetration, and good skin biocompatibility. It can efficiently cross the blood-brain barrier, regulate the neuroinflammatory microenvironment in the brain, significantly inhibit the colonization and progression of non-small cell lung cancer brain metastases, and strongly reverse tumor-related anxiety and depression-like mood disorders. It provides a novel drug delivery regimen with great clinical translational potential for the non-invasive synergistic treatment of brain tumors and their associated mood disorders.

This invention discloses a method for assessing the sensitivity of anticancer drugs based on patient-derived tumor tissue, belonging to the field of biomedical technology. The method includes: obtaining fresh tumor tissue from a patient and preparing it into tissue fragments of approximately 1 mm³; culturing the tissue fragments in vitro in a three-dimensional gel matrix; adding the anticancer drug to be tested to the culture system; digesting the tissue fragments after culture to obtain a single-cell suspension; and assessing drug sensitivity by detecting cell viability using flow cytometry. This invention can maximally preserve tumor heterogeneity and microenvironment, has a short operation cycle, and can quickly and accurately guide personalized clinical medication, especially suitable for solid tumors such as brain tumors.

The application provides a CaVM-Unet-based brain tumor MRI image segmentation method and device for missing modalities, and the method comprises the following steps: S1: acquiring a brain tumor MRI image dataset of four modalities, completing image preprocessing, data enhancement, modality missing marking and dataset division, and constructing a model standard input; S2: constructing a local feature extraction module and an intra-modality visual mamba module IVMB, independently extracting local features and capturing global dependency for each modality; S3: constructing a causally guided multi-modality interaction fusion module CGMIF, and realizing causally constrained multi-modality fusion under the condition of missing modalities; S4: constructing a dual regularization feature module DRFB, and stabilizing feature distribution and focusing on a tumor area in a decoding process; S5: training and optimizing the CaVM-Unet model by using a causally constrained loss and a multi-stage segmentation loss; and S6: segmenting a brain tumor by using the CaVM-Unet model for a multi-modality brain tumor MRI image.

The present application relates to a kind of brain tumor image segmentation method and system, belong to deep learning technical field, brain tumor image segmentation method is based on the brain tumor segmentation network of construction to brain tumor MRI image is segmented, brain tumor segmentation network includes encoder, decoder;Based on the feature extraction of the obtained brain tumor MRI image of encoder, obtain multiscale feature, wherein, the sub-encoder of encoder includes Conv-VMamba fusion module, Conv-VMamba fusion module is used to extract the local feature and global feature of brain tumor MRI image in parallel, and the local feature and global feature are spliced and channel mix wash, based on the decoding of multiscale feature, generate brain tumor image segmentation result, wherein, the sub-decoder of decoder includes dynamic channel sampling module, Conv-VMamba fusion module, improve the segmentation precision of brain tumor image.

The application relates to a brain tumor image segmentation method and device. The method comprises the following steps: acquiring a brain tomography image set; generating brain three-dimensional images of at least two scales according to the brain tomography image set; acquiring a median sagittal plane of each brain three-dimensional image respectively; performing tilt correction on the corresponding brain three-dimensional image according to the median sagittal plane respectively; and extracting a brain tumor image according to the brain three-dimensional image after tilt correction. The method can improve the brain tumor image segmentation accuracy.

The present invention relates to a brain tumor animal model that directly reflects the phenomenon in a human patient and a method of preparing the same, and more specifically, a brain tumor animal model that mutations are introduced into p53, Pten, and EGFR genes, a screening method of a therapeutic agent for a brain tumor using the animal model, and a preparing method thereof.

This invention provides a choline-inspired amphiphilic polymer nanodisc, the raw material for which includes a polymethacrylate copolymer; the polymethacrylate copolymer is prepared by polymerization of 2-methacryloyloxyethylphosphorylcholine and hexyl methacrylate. This choline-inspired amphiphilic polymer nanodisc possesses efficient BBB-crossing and brain-targeting capabilities. It can efficiently cross the BBB via choline transport receptors in both in vitro BBB models and in vivo in mice. The disc-shaped morphology and nanoscale size of the choline-inspired amphiphilic polymer nanodisc promote effective penetration of blood vessels, matrix, and cell barriers, exhibiting excellent solid tumor infiltration capabilities. This invention also provides a method for preparing the above-mentioned choline-inspired amphiphilic polymer nanodisc and its application in the preparation of brain tumor detection reagents or drugs for the prevention and treatment of brain tumors.

The presently disclosed subject matter provides compositions, methods, and kits for transfecting adipose-derived mesenchymal stem cells (AMSCs) in freshly extracted adipose tissue using nanoparticles comprising biodegradable polymers self-assembled with nucleic acid molecules. The presently disclosed subject matter also provides methods for treating a neurological disease in a patient in need thereof, the method comprising administering the AMSCs transfected with the nucleic acid molecules to the patient, wherein the nucleic acid molecules encode one or more bioactive molecules functional in the treatment of a neurological disease, particularly wherein the neurological disease is a brain tumor.

The present application relates to a kind of cross-domain federal medical image classification method and system based on FedSSAC, belong to medical image processing and federal learning technical field.The steps are as follows: (1) each medical client calls local labeled brain tumor MRI slice data set;(2) construct FedSSAC space-spectrum adaptive cooperation framework model;(3) each medical client completes FedSSAC space-spectrum adaptive cooperation framework model training and verification based on local data, and qualified parameter is uploaded to server, and server updates global model parameter by performance perception global aggregation strategy and is issued, and iteration training is converged to model;(4) the preprocessed MRI image to be classified is input into the FedSSAC space-spectrum adaptive cooperation framework model of training completion, and brain tumor classification result is output.

This invention discloses an automatic segmentation system and method for brain tumor MRI images, belonging to the field of medical image processing technology. It includes an encoder, a decoder, and the processing pathways connecting them. A feature calibration module is integrated into the encoder's downsampling stage. Through three-dimensional wavelet transform, spatial features are decomposed into sub-bands of different frequencies and differentially calibrated and fused to explicitly preserve and enhance detail information during feature compression. A cross-scale context gating unit is embedded in the skip connections. Attention weights are generated by aggregating multi-scale context information, intelligently filtering the transmitted encoder features before fusing them with decoder features. This method effectively solves the problems of detail loss due to downsampling and coarse feature fusion in traditional segmentation networks, significantly improving the accuracy and boundary fit of multi-sub-region segmentation of brain tumors.

This invention belongs to the field of traditional Chinese medicine technology, and relates to a topical Chinese medicine composition, topical drug, topical patch, acupoint patch, and preparation method and application for treating nausea and vomiting caused by chemotherapy for intracranial tumors. The Chinese medicine composition consists of the following raw materials in parts by weight: 1-2 parts of Ligusticum chuanxiong, 1-2 parts of Gastrodia elata, 2-4 parts of Uncaria rhynchophylla, 1-2 parts of Citrus aurantium, 1-2 parts of Sinapis alba, 1-2 parts of Clematis chinensis, 0.5-1 part of Melia toosendan, 0.5-1 part of Panax ginseng, 1-2 parts of Pinellia ternata, 1-2 parts of Albizia julibrissin bark, 0.5-1 part of Lentinus edodes, and 1-2 parts of Astragalus membranaceus. This Chinese medicine composition has the effects of relieving nausea and vomiting, soothing the liver and strengthening the spleen, detoxifying and eliminating turbidity, calming the liver and extinguishing wind, and calming the mind and soothing the nerves. It not only effectively controls the nausea and vomiting symptoms caused by chemotherapy for intracranial tumors, but also harmonizes the body's Qi and organ functions. The Chinese medicine composition has a simple formula, widely available ingredients, and a simple preparation method; it is for topical application, convenient for clinical use, and has low adverse reactions.

This application provides a method and system for image recognition and diagnosis of brain tumors in neurosurgery. First, the brain tumor image is preprocessed. The preprocessed brain tumor image is then decomposed into at least two feature component maps that complement each other in terms of structural and texture information. Next, each feature component map is sequentially subjected to a first-stage adaptive nonlocal mean filtering based on local statistical characteristics and a second-stage filtering based on structural correlation similarity measurement. The filtered feature component maps are then fused and reconstructed to obtain a feature-enhanced brain tumor image. This feature-enhanced brain tumor image is input into a pre-trained brain tumor recognition model, and the neurosurgical diagnosis results are obtained through the model. Using the scheme of this application, a two-stage adaptive filtering based on local scattering statistics and structural correlation measurement can be achieved, thereby enhancing tumor edge and texture features while suppressing ultrasound speckle, thus improving the accuracy of segmentation and diagnosis.

The application provides a task-driven brain tumor multi-modal MRI image fusion method and system, which comprises the following steps: inputting a multi-modal brain tumor MRI image into a generator, extracting local features and global features of the multi-modal brain tumor MRI image through a parallel convolutional neural network encoder and a Transformer encoder respectively; fusing the local features and the global features through a bidirectional connection feature fusion module, and reconstructing a fusion image through a decoder; inputting the fusion image into a discriminator; constructing and optimizing the generator and the discriminator through a joint loss function, so that the generator generates a fusion image suitable for the downstream task. Through the GAN network, the different tasks are integrated into the discriminator to promote the generator to generate a fusion result more suitable for the actual task application, so that the fusion image is further applied, and the accuracy of the downstream task is improved.

The application belongs to the technical field of pharmaceutical preparations, and specifically discloses an acetyl gastrodin transdermal patch and a preparation method thereof. The transdermal patch takes acetyl gastrodin as an effective component, is encapsulated in cubic phase liposomes composed of glycerol monostearate, cholesterol and Pluronic F127, is dispersed in a complex matrix of hydroxypropyl methyl cellulose and ethyl cellulose, and is matched with a complex transdermal enhancer of glyceryl caprylate / caprate and ethanol. In the preparation, the liposome material is first melted to load the drug, and then drug-loaded cubic phase liposomes are formed through ice water bath ultrasonic treatment. Then, the drug-loaded cubic phase liposomes are mixed with the polymer matrix and excipients, are defoamed, are coated, and are dried in two steps to be shaped. The cubic phase liposomes are used to significantly improve the drug penetration rate and stability, realize 24-hour smooth drug release, efficiently penetrate the skin and the blood-brain barrier, significantly inhibit non-small cell lung cancer brain metastases, improve tumor secondary anxiety and depression, have excellent skin compatibility, and provide a new non-invasive synergistic treatment scheme for brain tumors and concurrent emotional disorders.

The application discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and specifically comprises the following steps: acquiring patient brain multi-modal image data and extracting three-dimensional tumor regions and corresponding perfusion parameters; constructing a biological target region hierarchical structure containing tumor overall regions and hypoxic subregions; obtaining a relative hypoxia degree parameter by calculating the cerebral blood flow ratio of the hypoxic subregion to other regions of the tumor; deconstructing historical treatment case data to establish a dose-therapeutic effect correlation parameter set; training a dose adjustment strategy model for the hypoxic subregion; and generating a final radiotherapy dose plan scheme according to the dose adjustment value output by the model combined with a clinical guideline basic dose. The application overcomes the limitations of traditional uniform dose irradiation through a data-driven dose decision mechanism, thereby providing an objective and reliable evaluation basis for clinical selection and optimization of personalized treatment schemes.

GD2-specific chimeric antigen receptor effector cells for the treatment of solid tumors, which can be combined with an Enhancer of Zeste Homolog 2 (EZH2) inhibitor. ------ The present invention relates to GD2-specific chimeric antigen receptor (GD2.CAR) effector cells for the treatment of both solid tumors, particularly extracranial GD2+ tumors (such as soft tissue sarcomas and osteosarcomas, neuroblastomas, melanomas, lung cancers, bladder cancers, and retinoblastomas) and brain tumors. Furthermore, the present invention also relates to the use of GD2.CAR gene-modified effector cells in combination with an Enhancer of Zeste Homolog 2 (EZH2) inhibitor in the treatment of solid tumors.

This invention relates to a brain-targeting nanomedicine formulation for glioblastoma and its preparation method, belonging to the field of pharmaceutical technology. The formulation comprises an active pharmaceutical ingredient, an amphiphilic block copolymer carrier, a covalently bonded brain-targeting ligand, and a stabilizer. The preparation method includes: chemically coupling the targeting ligand to the carrier material; self-assembling the drug, modified carrier, and stabilizer into nanoparticles using a nanoprecipitation method; and obtaining the final product through membrane extrusion granulation, purification, and lyophilization. The resulting nanoparticles have uniform particle size, high drug loading, and good stability, and can simultaneously achieve efficient crossing of the blood-brain barrier and specific targeting of glioma cells. In vitro and in vivo experiments show that this formulation can significantly improve drug accumulation at brain tumor sites, inhibit tumor growth, and prolong the survival of model animals. Furthermore, the preparation process is controllable and easily scaled up.

This invention discloses a method, apparatus, medium, and device for detecting brain tumor lesions, relating to the field of image recognition technology. The method includes inputting acquired brain tumor image data into a pre-trained improved U-Net network to determine the denoised image data. The improved U-Net network adds a spatial attention module (SA) between the encoder and decoder of the original U-Net network. The denoised brain tumor image data is then input into an improved YOLOv8 detection model to obtain the brain tumor detection result. The improved YOLOv8 detection model replaces the C2f modules in the fourth and sixth layers of the original YOLOv8 model backbone network with MambaC2f modules.

This invention belongs to the field of intelligent medicine, specifically a brain tumor detection system based on optimized image segmentation, including a multimodal data collaborative acquisition module, a data quality enhancement module, a brain segmentation module, and a tumor detection module. This paper utilizes a content-aware adaptive grid to focus on brain image details and capture global structure. Gradient-weighted candidate label generation highlights key boundary regions, and boundary-aware importance scoring reduces computational complexity while retaining the most informative features. Uncertainty estimation and guided propagation improve the robustness of segmentation. This invention incorporates semantic labels from brain segmentation images as explicit priors into attention calculations, automatically assigning higher weights to high-risk brain regions. It dynamically modulates visual features using structured physiological data to achieve individualized image feature enhancement, establishing a deep interaction loop between visual and physiological information. This allows the two modalities to act as contexts for each other during decision-making, improving the accuracy of tumor detection.

The present invention relates to a drug delivery system with a micelle structure comprising a PEG2000-DSPE polymerized lipid and an APTEDB-PEG2000-DSPE polymer, and a preparation method thereof. The drug delivery system targets extra-domain B of fibronectin (EDB-FN), which is overexpressed in a brain tumor, and can pass through the blood-brain barrier (BBB) or the blood-brain tumor barrier (BBTB) to deliver a drug specifically to the brain tumor cells. In addition, the present invention can provide a pharmaceutical composition for diagnosing or treating a brain tumor, comprising the drug-loaded drug delivery system as an active ingredient. The composition can be accumulated inside the brain tumor and incorporated into the tumor cells to specifically inhibit tumor growth, and thus can be efficiently utilized for diagnosing or treating a brain tumor.

FIELD: neurosurgery and neurology.SUBSTANCE: after surgical access, a vessel located on the surface of the tumor tissue and extending into adjacent areas of the cortex is visualized. On both sides of the vessel, a bipolar electrode is used to coagulate the areas of the cortex affected by the tumor. Then, using microscissors, the arachnoid membrane is opened in the area of coagulation zones. An encephalotomy is performed. Next, subpial resection of the tumor in these areas is performed using an ultrasonic destroyer-aspirator, preserving the arachnoid membrane in which the preserved vessel passes. When visualizing a vessel located deep in the groove, dissection of the groove is performed while preserving the arachnoid membrane in which the vessel passes. The tumor around the vessel is resected using an ultrasonic destroyer-aspirator. In this case, first the part of the tumor on one side of the vessel is resected, then the part on the other side. These resection areas are connected to each other under the vessel. Then the residual tumor fragments on the vessel are removed. When visualizing a vessel passing through a tumor to healthy areas of the brain, this vessel is preserved, and the tumor tissue is separated from it along the periphery using an ultrasonic destroyer-aspirator. During the isolation of cortical and subcortical vessels, they are irrigated with saline solution. Then, through the resulting “intervascular windows,” deeply located tumor fragments are removed.EFFECT: method allows for the preservation of blood flow in cortical vessels during the removal of glial brain tumors, reducing the risk of developing ischemic complications in functionally significant areas of the cortex adjacent to the tumor resection zone, as well as in the subcortical structures of the brain, which, as a consequence, is associated with lower risks of developing postoperative neurological deficit.1 cl, 3 ex