Abstract: The present disclosure relates to biodegradable ureteral stents comprising an anti-cancer drug, and to a composition for use in medicine that may be used to ensure patency of a channel, namely a mammal ureter, for example, an obstructed ureter by a urinary stone, neoplasia or by a surgical procedure. The biodegradable ureteral stents (BUS) disclosed in the present disclosure unexpectedly allow a proper release of anti-cancer drugs, thus extending the duration of the treatment and increasing the efficacy of the treatment.

Abstract: An apparatus comprises a processing device configured to monitor for events associated with users interacting with an e-commerce platform, to identify an event type of a given event associated with a given user interacting with the e-commerce platform, and to select, based on the identified event type, at least one of a plurality of machine learning models configured to characterize different types of potentially malicious behavior on the e-commerce platform. The processing device is also configured to determine, utilizing the selected at least one machine learning model, whether the given user is exhibiting at least one of the different types of potentially malicious behavior. The processing device is also configured, responsive to determining that the given user is exhibiting at least one of the different types of potentially malicious behavior, to initiate actions on the e-commerce platform to prevent or mitigate an effect of the potentially malicious behavior.

Abstract: In some examples, a device includes a memory configured to store a first relationship between a blood pressure and another physiological parameter of a patient, the first relationship being indicative of cerebral autoregulation of the patient. The device also includes processing circuitry configured to receive first and second signals indicative of the blood pressure and the physiological parameter, respectively, of a patient. The processing circuitry is also configured to determine an expected value and an actual value of the physiological parameter at a particular blood pressure value of the first physiological signal. The processing circuitry is configured to determine, based on a difference between the actual value and the expected value, and store, in the memory, a second relationship between the blood pressure and the physiological parameter, the second relationship being indicative of a change in the cerebral autoregulation of the patient from the first relationship.

Abstract: An example system includes a camera system, memory, and processing circuitry coupled to the camera system and memory. The processing circuitry is configured to obtain image data and determine a visualization mask based on pixels associated with respiration. The processing circuitry is configured to determine a bounding box based on the visualization mask. The processing circuitry is configured to determine a sampling region for the image data based on an average including the bounding box and at least one previously determined bounding box. The processing circuitry is configured to determine at least a portion of a volume waveform based at least in part on the sampling region, determine at least one respiration parameter based on the volume waveform, and output, for display, the at least one respiration parameter.

Abstract: The current invention relates to a soft gummy for therapeutic oral use, said soft gummy comprising an outer shell encapsulating an inner liquid, wherein said outer shell comprises: (i) a pectin; and (ii) honey in an amount of at least 25 wt. % relative to the weight of the outer shell; wherein said inner liquid has a moisture content of at least 10 wt. % as measured by ISO 760:1978 and a viscosity of at most 50.0 Pa s measured at 20° C., and wherein the soft gummy comprises a hydrolysable tannin or source of hydrolysable tannin. The invention also relates to a use of a soft gummy.

Abstract: In some examples, a device includes a memory configured to store a first relationship between a blood pressure and another physiological parameter of a patient, the first relationship being indicative of cerebral autoregulation of the patient. The device also includes processing circuitry configured to receive first and second signals indicative of the blood pressure and the physiological parameter, respectively, of a patient. The processing circuitry is also configured to determine an expected value and an actual value of the physiological parameter at a particular blood pressure value of the first physiological signal. The processing circuitry is configured to determine, based on a difference between the actual value and the expected value, and store, in the memory, a second relationship between the blood pressure and the physiological parameter, the second relationship being indicative of a change in the cerebral autoregulation of the patient from the first relationship.

Abstract: In some examples, a system is configured to determine, using a neural network algorithm of a cerebral autoregulation model, a cerebral autoregulation status of the patient based at least in part on a blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

Abstract: In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

Abstract: Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Abstract: In some examples, a system is configured to determine, using a neural network algorithm of a cerebral autoregulation model, a cerebral autoregulation status of the patient based at least in part on a blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

Abstract: Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Abstract: Implementations described herein disclose a method of determining how well predictions of an impending hypoxia are reported in real-time, so that a user has higher confidence in the reported predictions.

Abstract: In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

Abstract: Implementations described herein disclose an artificial intelligence (AI) based method for generating an oxygen saturation level output signal using the trained neural network. In one implementation, the method includes receiving a photoplethysmographic (PPG) signal, the PPG signal including a red PPG signal and an infrared PPG signal, generating an input feature matrix by performing time-frequency transform of the PPG signal, training a neural network using the input feature matrix and an oxygen saturation level input signal, and generating an oxygen saturation level output signal using the trained neural network.

Abstract: In some examples, a system is configured to determine a non-cerebral autoregulation status value of a patient using machine learning. In some examples, processing circuitry of the system is configured to determine, using a neural network algorithm that has been trained via machine learning training, an individualized adjustment value that is individualized for the patient, including inputting physiological data associated with the patient. The processing circuitry may determine a non-cerebral autoregulation status of the patient based on a cerebral autoregulation value of the patient based on the non-cerebral autoregulation status value of the patient and the adjustment value.

Abstract: In some examples, a system includes an oxygen saturation sensing device configured to sense an oxygen saturation level of a patient and processing circuitry. The processing circuitry may be configured to receive a signal indicative of the oxygen saturation level of the patient, determine that the signal indicates the oxygen saturation level is at or below a desaturation threshold, and in response to determining the oxygen saturation level of the patient is at or below the desaturation threshold, predict, using an oxygen saturation prediction model, whether the oxygen saturation level of the patient will increase above the desaturation threshold by the end of a predefined time period. In response to predicting that the oxygen saturation level of the patient will increase above the desaturation threshold by the end of the predefined time period, the processing circuitry refrains from outputting an indication of the patient experiencing an oxygen desaturation event.

Abstract: Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

Abstract: In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

Abstract: In some examples, a system is configured to determine, using a neural network algorithm of a cerebral autoregulation model, a cerebral autoregulation status of the patient based at least in part on a blood pressure of the patient over a period of time and regional cerebral oxygen saturation of the patient over the period of time.

Abstract: Implementations described herein discloses, a method of AI based video tagging for alarm management includes receiving, using a processor, a video stream, the video stream comprising a sequence of images for at least a portion of a patient, determining, using the processor, a physiological parameter for the patient based on the sequence of images, detecting, using machine learning, presence of a noise object and setting a interaction-flag to a positive value in response to detecting the noise object, comparing a quality level of the sequence of images with a threshold quality level, and modifying an alarm level based on the value of the interaction-flag and comparison of the quality level of the sequence of depth images with the threshold quality level.