A system for displaying an intraoperative user interface for a surgical procedure is disclosed. The system comprises an arthroscopic imaging device, a display device, an input device, a processor, and a non-transitory, computer-readable medium. The arthroscopic imaging device is configured to capture one or more images of a patient anatomy within a joint of a patient. The input device is configured to receive input from a user. The processor is configured to receive the images from the arthroscopic imaging device and display a user interface on the display device that includes the images from the arthroscopic imaging device. The processor is also configured to receive the input from the input device, display one or more visual overlays over the one or more images based on the input, and apply one or more digital markers in a fixed location on the one or more images based on the input.

Methods and systems for deconvoluting tumor ecosystems for personalized cancer therapy are disclosed. Generally, human cancers exhibit large variation in behavior between and within patients, which is in large part related to cellular composition. Identifying cell types can identify specific types of tumors and/or cancers present in an individual. Further embodiments generally describe identifying therapies from clinical trials to which the tumor or cancer ecotypes respond, thus providing personalized therapies based on the identified cancer or tumor type.

Disclosed herein are methods and systems for identifying a drug capable of changing a cell's state, function, and/or predicted age, which is useful in, at least, drug discovery. Further disclosed herein are methods and systems for identifying an in vitro cell's state, function, and/or predicted age.

The invention relates to a medical diagnosis assistance system, a medical diagnosis assistance method, and a training method for training an artificial intelligence entity. The medical diagnosis assistance system (100) comprises: an input interface (110) configured to receive medical image data (1) of a patient; a computing device (150) configured to implement: a classification module (151) configured to classify parts of interest, POI (10, 11, 12, 13, 14, 15, 20, 30), comprising objects of interest, OOI, and/or regions of interest, ROI, within the received medical image data (1), and to assign a corresponding reliability metric to each of the classified POI (10, 11, 12, 13, 14, 15, 20, 30); an analysis module (152) configured to determine, based on the POI (10, 11, 12, 13, 14, 15, 20, 30) and the assigned reliability metric, an analysis of the medical image data (1); and an output interface (190) configured to output an output signal (71) indicating the analysis.

The present disclosure belongs to the field of petroleum engineering, and specifically relates to a subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network. The subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network includes three steps: digital twin model establishment, degradation process re-prediction model establishment, and remaining useful life calculation model establishment. The subsea Christmas tree re-prediction system integrating Kalman filter and Bayesian network includes a subsea distribution unit information acquisition subsystem mounted on an subsea distribution unit, a subsea control module information acquisition subsystem mounted on a subsea control module, a subsea valve bank information acquisition subsystem mounted on a subsea valve bank, a wellhead mechanical module information acquisition subsystem mounted on a wellhead mechanical module, a subsea environmental information acquisition module mounted on a subsea control module, and a subsea Christmas tree digital twin subsystem mounted in an overwater control station.

A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.

A system has a plurality of diagnostic sensors and a first computing device interconnected to the plurality of diagnostic sensors for: commanding the plurality of diagnostic sensors to collect diagnostic data from a patient, determining if each of the plurality of diagnostic sensors are positioned at respectively predefined body locations of the patient, combining the collected diagnostic data for analysis when all of the plurality of diagnostic sensors are positioned at respectively predefined body locations of the patient, and prompting the patient to reposition at least one of the plurality of diagnostic sensors when the at least one sensor is determined as being mis-positioned from the corresponding predefined body location.

A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.

Methods for diagnosis of sepsis are disclosed. In particular, the invention relates to the use of bio -markers for aiding diagnosis, prognosis, and treatment of sepsis, and to a panel of biomarkers that can be used to distinguish sepsis from noninfectious sources of inflammation, such as caused by traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS).

The present inventions describes a Facile Accelerated Specific Therapeutic (FAST) pipeline to rapidly design, built and test peptide nucleic acid treatments against mammalian or microbial genes of interest. The invention may include a bioinformatics application for facile and accelerated high throughput design of peptide nucleic acids (PNAs) that act as inhibitors of expression of specific targeted genes by binding to their mRNA to block translation, or PNA activators that can activate expression of target genes by binding to the respective promoter regions and recruitment of transcriptional activators. The invention may further involve automated and high throughput parallel synthesis of a PNA inhibitor/activator library for generation of on-site therapeutic molecules, which may reduce storage requirements, and the development of efficient delivery of therapeutic PNAs to host cells to overcome challenges of transport, toxicity, and bioavailability. The invention may further involve the testing of designed and built PNAs in a high throughput manner in a relevant infection, or mammalian cell culture model. The proposed invention may allow identification of important gene targets, and quickly generate translatable therapies that can be tested under host conditions, and most importantly develop a countermeasure platform that can be deployed on-site in the future to generate therapies in short time scales.