Evaluating the efficacy of uniformly designed square mesh resin 3D printed scaffolds in directing the orientation of electrospun PCL nanofibers.

Replicating the architecture of extracellular matrices (ECM) is crucial in tissue engineering to support tissues' natural structure and functionality. The ECM's structure plays a significant role in directing cell alignment. Electrospinning is an effective technique for fabricating nanofibrous substrates that mimic the architecture of extracellular matrices (ECM).

Authorship equity in global health research: who gets the credit at University of California, San Francisco?

Authorship inequity exists in global health research and can be representative of unequal partnerships. Previous studies showed that low-income and middle-income country (LMIC) authors are under-represented in publications from global collaborative research between LMIC and high-income countries (HIC). However, there are little data on trends for how specific HIC institutions are performing concerning equitable authorship.

Acute Experiences of Negative Interpersonal Interactions: Examining the Dynamics of Negative Mood and Ambulatory Blood Pressure Responses Among Black and Hispanic Urban Adults.

Background: Negative interpersonal interactions are associated with acute increases in ambulatory blood pressure (ABP). Yet, the mechanisms underlying this relationship are unclear.

Purpose: This study tested whether negative interpersonal interactions predict higher ABP both in the moment and during subsequent observations, and whether increases in negative mood mediate these relations.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (aby and nfant rain egmentation Neural work), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations.