Exploring the Role of Communication Asset Mapping (CAM) as a Strategy to Promote Hereditary Cancer Risk Assessment Information Within African American Communities.

: African Americans (AAs) carry the largest burden for almost every type of cancer in the US and are also more likely to die from cancer. Approximately 10% of cancers can be explained by a hereditary factor and detected earlier. Many AAs, however, have inequitable access to hereditary cancer risk assessment (HCRA) tools and information, further exacerbating disparities in cancer rates.

Evaluation of disparities in hospitalisation outcomes for deaf and hard of hearing patients with COVID-19: a multistate analysis of statewide inpatient databases from Florida, Maryland, New York and Washington.

Objective: Investigate whether deaf or hard of hearing (D/HH) patients with COVID-19 exhibited different hospitalisation outcomes compared with hearing patients with COVID-19.

Design: Cohort study SETTING: Statewide Inpatient Databases for Florida, Maryland, New York and Washington, for the year 2020.

Participants: Records of patients aged 18-64 years with COVID-19 PRIMARY OUTCOMES AND MEASURES: Differences in in-hospital death, 90-day readmission, length of stay, hospitalisation cost, hospitalisation cost per day, intensive care unit (ICU) or coronary care unit (CCU) utilisation and ventilation use were evaluated.

How do we capture child and family goals? Examining the goal setting practices used by community-based allied health professionals.

Purpose: This study aimed to i) identify child and family goals reported in a community allied health service, ii) map goals to ICF domains, and iii) evaluate goal characteristics against child-centred and family-centred practice principles.

Methods: A retrospective study design was used to extract and analyse raw goal data documented for children and families accessing a community-based allied health service. ICF linking rules were used to map goals to the ICF domains.

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.

Tactile edges and motion via patterned microstimulation of the human somatosensory cortex.

Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin.