Perceived Social Support, Intolerance of Uncertainty, and War-Related Stress: Unraveling the Nexus with Treatment Burden in Adult-Child Caregivers During War.

Adult-child caregivers encounter various challenges due to their array of roles and tasks, often leading to a substantial sense of treatment burden. While previous research has explored factors contributing to treatment burden, much of it has centered on routine periods, leaving a gap in the understanding of this issue during crisis situations characterized by heightened stress and uncertainty. Therefore, this study aims to address this gap by investigating the mediating role of intolerance of uncertainty and war-related stress in the relationship between perceived social support and treatment burden among adult-child caregivers during the Israel-Hamas war.

"I Want to Tell You Something, but Not Here": Governmental Inspection teams' Challenges in Identifying Mistreatment in Nursing Homes.

This study explores the challenges faced by the Ministry of Health's inspection teams in identifying mistreatment within Israeli nursing homes. Four focus groups with 19 multidisciplinary inspectors revealed two main themes. First, the interaction between nursing home management, staff, and inspectors ranged from denial and concealment of mistreatment to cooperation in reporting it.

The oncogenic axis YAP/MYC/EZH2 impairs PTEN tumor suppression activity enhancing lung tumorigenicity.

The tumor suppressor PTEN (phosphatase and tensin homolog deleted in chromosome 10) is genetically deleted or downregulated in many cancer types. Loss of PTEN protein expression is frequently found in lung cancer while genetic alterations are less abundant. PTEN expression is regulated at multiple genetic and epigenetic levels and even partial reduction of its expression increases cancer occurrence.

Computational pipeline predicting cell death suppressors as targets for cancer therapy.

Identification of promising targets for cancer therapy is a global effort in precision medicine. Here, we describe a computational pipeline integrating transcriptomic and vulnerability responses to cell-death inducing drugs, to predict cell-death suppressors as candidate targets for cancer therapy. The prediction is based on two modules; the transcriptomic similarity module to identify genes whose targeting results in similar transcriptomic responses of the death-inducing drugs, and the correlation module to identify candidate genes whose expression correlates to the vulnerability of cancer cells to the same death-inducers.