The art of diagnosing rare skin tumors: Can DL-CNNs enhance dermatologists' diagnostic accuracy?

Importance: Deep learning convolutional neural networks (DL-CNN) achieved diagnostic accuracies comparable to dermatologists in controlled test environments. However, their performance in diagnosing rare skin tumors (RST) remains unclear. This study aimed to evaluate a binary DL-CNN's diagnostic performance in RST and assess the level of support for an international group of dermatologists.

OCT-Defined Atrophic Age-Related Macular Degeneration Changes Associated with Deep Visual Sensitivity Losses: A Multicenter Study.

Purpose: To identify combination(s) of OCT changes that define atrophic age-related macular degeneration (AMD) lesions associated with repeatable deep visual sensitivity defects.

Design: Reader study.

Participants: One hundred seventy-one OCT scans from 60 eyes of 53 participants.

Tumor cell-adipocyte gap junctions activate lipolysis and contribute to breast tumorigenesis.

A pro-tumorigenic role for adipocytes has been identified in breast cancer, and reliance on fatty acid catabolism found in aggressive tumors. The molecular mechanisms by which tumor cells coopt neighboring adipocytes, however, remain incompletely understood. Here, we describe a direct interaction linking tumorigenesis to adjacent adipocytes.

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

Importance: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM.

Transient colonization by Priestia megaterium B1L5 alters the structure of the rhizosphere microbiome towards potential plant beneficial bacterial groups in apple plantlets.

Background: Plant growth-promoting bacteria (PGPB) can beneficially modulate rhizosphere microbial communities, potentially improving plant health and reducing disease incidence. Limited research exists on the influence of PGPB inoculation on the rhizosphere microbial communities of apple plants, particularly in soils affected by apple replant disease (ARD). Here, we evaluated the capacity of GFP-labelled B1 (designated as B1L5) to colonize the roots of apple plantlets grown in two soils: ARD-affected soil and ARD-unaffected grass soil.