TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues.

The spatial arrangement of cells plays a pivotal role in shaping tissue functions in various biological systems and diseased microenvironments. However, it is still under-investigated of the topological coordinating rules among different cell types as tissue spatial patterns. Here, we introduce the Triangulation cellular community motif Neural Network (TrimNN), a bottom-up approach to estimate the prevalence of sizeable conservative cell organization patterns as Cellular Community (CC) motifs in spatial transcriptomics and proteomics.

Assessing the effect of chest orientation on pulmonary pressure under free field shock waves: a numerical analysis.

: This study explores how thoracic orientation affects lung pressure and injury outcomes from shock waves, building on earlier research that suggested human posture impacts injury severity. : A layered finite element model of the chest was constructed based on the Chinese Visual Human Dataset (CVH), including the rib and intercostal muscle layers. The dynamic response of the chest under 12 different angle-oriented shock waves under incident pressures of 200 kPa and 500 kPa was calculated.

Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.

Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.

Effectiveness of room-of-error interventions for healthcare providers: a systematic review.

Background: Patient safety incidents are recognized as significant contributors to patient mortality, thus demanding immediate attention and strategic interventions in healthcare systems. The room-of-error education program serves as a solution, as it provides a case-based learning platform allowing nursing students to identify and resolve medical errors within a controlled environment systematically. This study aimed to identify the context, mechanisms, and outcomes of room-of-error training programs.

SEMI-AUTOMATED EXTRACTION OF RESEARCH TOPICS AND TRENDS FROM NCI FUNDING IN RADIOLOGICAL SCIENCES FROM 2000-2020.

Background: Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding.

Purpose: We present a semi-automated analysis of 21 years of R-type National Cancer Institute (NCI) grants to departments of radiation oncology and radiology using natural language processing (NLP).

Methods: We selected all non-education R-type NCI grants from 2000 to 2020 awarded to departments of radiation oncology/radiology with affiliated schools of medicine.