Systems, methods, and/or apparatuses may be operative to perform a dialysis process that includes a displacer infusion process. In one embodiment, a method for determining a displacer compound may include constructing a plurality of target protein quantitative structure-activity relationship (QSAR) models, one for each of the plurality of binding sites, analyzing a set of candidate compounds using the plurality of QSAR models to determine a set of at least one potential compound with an affinity for binding to each of the plurality of binding sites, and selecting at least one displacer compound from the set of at least one potential compound. Other embodiments are described.

Systems, methods, and/or apparatuses may be operative to perform a dialysis process that includes a displacer infusion process. The dialysis machine may include at least one processor and a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the processor, may cause the at least one processor to access dialysis information for a dialysis process performed by a dialysis machine, the dialysis information indicating a target substance to be displaced from a binding compound by a displacer, and determine an infusion profile for infusing the displacer into a patient during a displacer infusion process of the dialysis process, the infusion profile determined based on the dialysis information and an infusion constraint. Other embodiments are described.

A system and method is provided for training operators (e.g., users), including new nurses or nephrologists as well as patients or their family members, to learn the intricacies of dialysis (e.g., hemodialysis) treatment through a simulation. The simulation may afford users the ability to fail catastrophically in simulated dialysis treatment, see the consequences, learn and internalize complex algorithms, and develop the ability to think in a time pressured situation. The simulation may thereby provide a safe learning environment for experiencing decisions and consequences of these decisions when performing a dialysis treatment. In some instances, the simulation may be in a mobile application form factor to encourage more ubiquitous use on personal devices, which may provide ease of access for the dialysis training. The simulation may further include time-based simulated scenarios that increase in difficulty and complexity between levels.

A processor of a medical device configured to communicate with a remote server can be programmed to protect the medical device from exposure to unauthorized or malicious software. A system or method to implement this form of protection can include, for example, at least one processor on the medical device, a control software module that controls the operation of the medical device and is executable on the processor, a data management module that manages data flow to and from the control software module from sources external to the medical device, and an agent module that has access to a limited number of designated memory locations in the medical device. In addition, a hemodialysis apparatus can be configured to operate in conjunction with an apparatus for providing purified water from a source such as a municipal water supply or a well. A system for controlling delivery of purified water to the hemodialysis apparatus can comprise a therapy controller of the hemodialysis apparatus configured to communicate with a controller of a water purification device, and a user interface controller of the hemodialysis apparatus configured to communicate with the therapy controller, and to send data to and receive data from a user interface.

A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.

A method for determining the dry weight of a patient after dialysis therapy, wherein the patient's blood volume is monitored and blood volume values are output. The blood volume values are recorded and evaluated for a predetermined period of time after reaching an ultrafiltration volume appropriately predetermined for the patient, wherein the dry weight of the patient then is determined on the basis of the rate of change of the blood volume during the predetermined period of time.

The described technology generally includes systems and processes for a PD optimization process may operate to estimate, predict, or otherwise determine the value of PD dose variables values based on patient characteristics and/or PD prescription information. In one embodiment, a PD optimization process may be or may include a UFV determination process, operative to determine a predicted UFV for a patient. In some embodiments, the UFV determination process may include training a computational model to generate a predicted UFV output based on input of patient characteristics, PD prescription information, PD outcomes (for instance, UFV), and/or historical information associated with patient characteristics, PD prescription information, and/or PD outcomes. In some embodiments, a feedback control process with continuous Intraperitoneal Pressure (IPP) and hydration status measurements may be used to keep the hydration status of the patient within a target level ran. Other embodiments are described.

A modified hemofiltration method for clearing peripheral α-synuclein aggregates in patients with neurodegenerative diseases is provided, which falls into the field of medicine. Specifically, a ratio S of synuclein dimers in blood is obtained; a blood flow velocity and an exchange membrane area for hemofiltration are determined through clinical trial data or historical literature data; hemofiltration is performed by the determined blood flow velocity and exchange membrane area, a calculation model of the ratio S of different synuclein dimers and an exchange membrane aperture D required for hemofiltration is constructed by linear regression; a clearance rate of synuclein dimers can be estimated by setting hemofiltration parameters with the calculation model. It is found that hemofiltration is beneficial to reducing the level of peripheral α-synuclein aggregates in patients with neurodegenerative diseases. Therefore, the calculation model is constructed, which provides scientific data and a new solution for clinically relieving α-synuclein-related toxicity symptoms.

Dialysis systems comprising actuators that cooperate to perform dialysis functions and sensors that cooperate to monitor dialysis functions are disclosed. According to one aspect, such a hemodialysis system comprises a user interface model layer, a therapy layer, below the user interface model layer, and a machine layer below the therapy layer. The user interface model layer is configured to manage the state of a graphical user interface and receive inputs from a graphical user interface. The therapy layer is configured to run state machines that generate therapy commands based at least in part on the inputs from the graphical user interface. The machine layer is configured to provide commands for the actuators based on the therapy commands.

A portable hemodialysis system is provided including a dialyzer, a closed loop blood flow path which transports blood from a patient through the dialyzer and back to the patient, and a closed loop dialysate flow path which transports dialysate through the dialyzer. Preferably, the hemodialysis system includes a sorbent filter in the dialysate flow path. Furthermore, the hemodialysis machine includes a blood pump, and a pair of dialysate pumps. A processor controls the flow of blood through the blood flow path, and the processor controls the flow of dialysate through the dialysate flow path. In addition, the processor stores a patient treatment plan wherein the flow rate of the dialysate through the dialysate flow path reduces throughout the patient's treatment to maximize the amount of urea removed by the sorbent filter.