Abstract: One or more systems for automated preparation of a regenerative epidermal suspension comprises a base unit, a cartridge assembly containing a cup, and a tissue disaggregator. The system may also include related tools. The system executes one or more sequences wherein a first motor actuates the pestle against a tissue sample that is disposed along a screen within the cup while the tissue sample is in contact with, in sequence, an enzyme solution, a first buffer solution, and then a second buffer solution. To transition between fluidic contact stages or steps, the tissue disaggregator engages with and lifts the cup into a raised position within a processing area of the cartridge while a base plate motor rotates the appropriate well into alignment under the cup for each processing step. Once the sequence is complete, a user may draw up a regenerative epidermal suspension through an opening in the cartridge top cover.

Abstract: One or more methods for automated preparation of a regenerative epidermal suspension comprises a processor receiving an initiation signal that indicates that a cartridge that has a cover, a cup containing a tissue sample, and a well plate situated beneath the cover that has a first well containing an enzyme solution and a second well containing a buffer solution, has been placed on a sensor, executing a sequence that actuates a tissue disaggregator against the tissue sample in the cup positioned in the first well, raises the cup to an upper position, rotates the well plate to position the second well beneath the cup, lowers the cup to a position within the second well, and actuates the tissue disaggregator against the tissue sample in the second well.

Abstract: One or more methods for treating a tissue site comprises collecting a tissue sample that has keratinoytes and placing the tissue sample in a cartridge that has a well plate, positioning the cartridge within a base unit that has a tissue disaggregator, activating the base unit to rotate the well plate to align wells in the well plate with the tissue sample and the tissue disaggregator, operate the tissue disaggregator to separate the tissue sample to form a regenerative epidermal suspension, and provide the regenerative epidermal suspension to a tissue site to enhance healing of the tissue site.

Abstract: One or more systems for automated preparation of a regenerative epidermal suspension comprises a base unit, a cartridge assembly containing a cup, and a tissue disaggregator. The system may also include related tools. The system executes one or more sequences wherein a first motor actuates the pestle against a tissue sample that is disposed along a screen within the cup while the tissue sample is in contact with, in sequence, an enzyme solution, a first buffer solution, and then a second buffer solution. To transition between fluidic contact stages or steps, the tissue disaggregator engages with and lifts the cup into a raised position within a processing area of the cartridge while a base plate motor rotates the appropriate well into alignment under the cup for each processing step. Once the sequence is complete, a user may draw up a regenerative epidermal suspension through an opening in the cartridge top cover.

Abstract: One or more methods for treating a tissue site comprises collecting a tissue sample that has keratinoytes and placing the tissue sample in a cartridge that has a well plate, positioning the cartridge within a base unit that has a tissue disaggregator, activating the base unit to rotate the well plate to align wells in the well plate with the tissue sample and the tissue disaggregator, operate the tissue disaggregator to separate the tissue sample to form a regenerative epidermal suspension, and provide the regenerative epidermal suspension to a tissue site to enhance healing of the tissue site.

Abstract: One or more cartridges for preparation of a regenerative epidermal suspension comprises a cover with a raised processing opening, a cup that can be received within the raised processing opening and has a screen, and a well plate that is positioned under the cover, has a well, and rotates relative to the cover such that the well aligns with the raised processing opening. One or more cartridges for preparation of a regenerative epidermal suspension comprises a cover with an opening that can receive a tissue sample.

Abstract: One or more methods for automated preparation of a regenerative epidermal suspension comprises a processor receiving an initiation signal that indicates that a cartridge that has a cover, a cup containing a tissue sample, and a well plate situated beneath the cover that has a first well containing an enzyme solution and a second well containing a buffer solution, has been placed on a sensor, executing a sequence that actuates a tissue disaggregator against the tissue sample in the cup positioned in the first well, raises the cup to an upper position, rotates the well plate to position the second well beneath the cup, lowers the cup to a position within the second well, and actuates the tissue disaggregator against the tissue sample in the second well.

Abstract: A bag comprises a front face, a rear face, a first side face comprising a first side pocket, a second side face, a top face and a bottom face. The bottom face comprises a rigid base. A storage region is positioned between the front face, the rear face, the first side face, the second side face, the top face and the bottom face. An access mechanism is constructed and arranged to allow access to the storage region. Four wheels are coupled to the bottom face. An expansion mechanism is constructed and arranged to expand the storage region.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: A surgical clip application device for applying and crimping surgical clips to ligate a vessel.

Abstract: A learning system includes a non-transitory memory, and one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including generating a user knowledge mesh including generating topic nodes each corresponding to a topic included in the user knowledge mesh, and generating concept nodes each corresponding to a key learnable concept, wherein each of the topic nodes is connected to another one of the topic nodes, each of the concept nodes is connected to one of the topic nodes, and each of the key learnable concepts includes one or more interactions related to the key learnable concept.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: A learning system includes a non-transitory memory, and one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including generating a user knowledge mesh including generating topic nodes each corresponding to a topic included in the user knowledge mesh, and generating concept nodes each corresponding to a key learnable concept, wherein each of the topic nodes is connected to another one of the topic nodes, each of the concept nodes is connected to one of the topic nodes, and each of the key learnable concepts includes one or more interactions related to the key learnable concept.

Abstract: A vibratory myofascial decompression apparatus includes a cup and at least one electrically operated vibration element positioned external to the cup. The cup includes a cup body defining an internal cavity and a lip that defines an opening into the internal cavity, with the lip being configured to contact a skin surface of a user. The cup also includes an exhaust port positioned inside the internal cavity configured to permit air to be withdrawn from the internal cavity. The at least one electrically operated vibration element is coupled with the cup to permit vibration to be transmitted through the cup body and the lip to the skin of the user.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: A bag comprises a front face, a rear face, a first side face comprising a first side pocket, a second side face, a top face and a bottom face. The bottom face comprises a rigid base. A storage region is positioned between the front face, the rear face, the first side face, the second side face, the top face and the bottom face. An access mechanism is constructed and arranged to allow access to the storage region. Four wheels are coupled to the bottom face. An expansion mechanism is constructed and arranged to expand the storage region.

Abstract: A learning system includes a non-transitory memory, and one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including generating a user knowledge mesh, wherein the operation of generating the user knowledge mesh includes generating a plurality of topic nodes, each of the plurality of topic nodes corresponding to a topic included in the user knowledge mesh, and generating a plurality of concept nodes, each of the plurality of concept nodes corresponding to a key learnable concept, wherein each of the plurality of topic nodes is connected to another one of the plurality of topic nodes, each of the plurality of concept nodes is connected to one of the plurality of topic nodes, and each of the plurality of key learnable concepts includes one or more interactions related to the key learnable concept.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.