Systemically administered platelet-inspired nanoparticles to reduce inflammation surrounding intracortical microelectrodes.

Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP).

Artificial intelligence-generated feedback on social signals in patient-provider communication: technical performance, feedback usability, and impact.

Objectives: Implicit bias perpetuates health care inequities and manifests in patient-provider interactions, particularly nonverbal social cues like dominance. We investigated the use of artificial intelligence (AI) for automated communication assessment and feedback during primary care visits to raise clinician awareness of bias in patient interactions.

Materials And Methods: (1) Assessed the technical performance of our AI models by building a machine-learning pipeline that automatically detects social signals in patient-provider interactions from 145 primary care visits.

Attitudes toward and preparedness for lung transplantation among individuals with cystic fibrosis in the era of highly effective modulators.

Background: Outcomes for individuals with cystic fibrosis (CF) have improved due to highly effective modulator therapy (HEMT). However, lung transplant (LTx) remains an important treatment for people with advanced lung disease. This study assessed attitudes and knowledge about LTx in the HEMT era.

Designing Communication Feedback Systems To Reduce Healthcare Providers' Implicit Biases In Patient Encounters.

Healthcare providers' implicit bias, based on patients' physical characteristics and perceived identities, negatively impacts healthcare access, care quality, and outcomes. Feedback tools are needed to help providers identify and learn from their biases. To incorporate providers' perspectives on the most effective ways to present such feedback, we conducted semi-structured design critique sessions with 24 primary care providers.

ConverSense: An Automated Approach to Assess Patient-Provider Interactions using Social Signals.

Patient-provider communication influences patient health outcomes, and analyzing such communication could help providers identify opportunities for improvement, leading to better care. Interpersonal communication can be assessed through "social-signals" expressed in non-verbal, vocal behaviors like interruptions, turn-taking, and pitch. To automate this assessment, we introduce a machine-learning pipeline that ingests audio-streams of conversations and tracks the magnitude of four social-signals: dominance, interactivity, engagement, and warmth.