Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty.

Purpose: Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA).

Methods: A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases.

Predicting robotic-assisted total knee arthroplasty operating time : benefits of machine-learning and 3D patient-specific data.

Aims: No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model.

Methods: A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system.

N-Doped Carbon Dot Hydrogels from Brewing Waste for Photocatalytic Wastewater Treatment.

The brewery industry annually produces huge amounts of byproducts that represent an underutilized, yet valuable, source of biobased compounds. In this contribution, the two major beer wastes, that is, spent grains and spent yeasts, have been transformed into carbon dots (CDs) by a simple, scalable, and ecofriendly hydrothermal approach. The prepared CDs have been characterized from the chemical, morphological, and optical points of view, highlighting a high level of N-doping, because of the chemical composition of the starting material rich in proteins, photoluminescence emission centered at 420 nm, and lifetime in the range of 5.

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis.

Background: Predictive modeling promises to improve our understanding of what variables influence patient satisfaction after total knee arthroplasty (TKA). The purpose of this article was to systematically review the relevant literature using predictive models of clinical outcomes after TKA. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes.

Glucose Control, Disease Burden, and Educational Gaps in People With Type 1 Diabetes: Exploratory Study of an Integrated Mobile Diabetes App.

Background: Self-monitoring and self-management, crucial for optimal glucose control in type 1 diabetes, requires many disease-related decisions per day and imposes a substantial disease burden on people with diabetes. Innovative technologies that integrate relevant measurements may offer solutions that support self-management, decrease disease burden, and benefit diabetes control.

Objective: The objective of our study was to evaluate a prototype integrated mobile phone diabetes app in people with type 1 diabetes.

Cost-Effectiveness Analysis in Telehealth: A Comparison between Home Telemonitoring, Nurse Telephone Support, and Usual Care in Chronic Heart Failure Management.

Objectives: To assess the cost effectiveness of home telemonitoring (HTM) and nurse telephone support (NTS) compared with usual care (UC) in the management of patients with chronic heart failure, from a third-party payer's perspective.

Methods: We developed a Markov model with a 20-year time horizon to analyze the cost effectiveness using the original study (Trans-European Network-Home-Care Management System) and various data sources. A probabilistic sensitivity analysis was performed to assess the decision uncertainty in our model.

Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study.

Background: The lack of proven efficacy of new healthcare interventions represents a problem for health systems globally. It is partly related to suboptimal implementation processes, leading to poor adoption of new interventions. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) aims to study current existing health Risk Stratification (RS) strategies and tools on frail elderly patients.

Fast mental states decoding in mixed reality.

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity.

Prediction of P300 BCI aptitude in severe motor impairment.

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm.

Semantic conditioning of salivary pH for communication.

Objectives: Semantic conditioning of salivary pH was investigated as a new paradigm for binary communication.

Methods And Materials: In a sample of eleven healthy participants, affirmation or negation of presented statements were paired with milk and lemon to condition changes in salivary pH level.

Results: Significant differences between the conditioned reactions were found at the group level.

Brain communication in the locked-in state.

Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state.

Semantic classical conditioning and brain-computer interface control: encoding of affirmative and negative thinking.

The aim of the study was to investigate conditioned electroencephalography (EEG) responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI). In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs) were paired with two different tones which served as unconditioned stimuli (USs). The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs).

Brain-computer interface and semantic classical conditioning of communication in paralysis.

We propose a classical semantic conditioning procedure to allow basic yes-no communication in the completely locked-in state as an alternative to instrumental-operant learning of brain responses, which is the common approach in brain-computer interface research. More precisely, it was intended to establish cortical responses to the trueness of a statement irrespective of the particular constituent words and letters or sounds of the words. As unconditioned stimulus short aversive stimuli consisting of 1-ms electrical pulses were used.

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas.

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1).