AGITG MONARCC: A Randomized Phase 2 Study of Panitumumab Monotherapy and Panitumumab Plus 5-Fluorouracil as First-Line Therapy for Older Patients With RAS and BRAF Wild Type Metastatic Colorectal Cancer. A Study by the Australasian Gastro-Intestinal Trials Group (AGITG).

Background: Panitumumab (pan) plus chemotherapy is a preferred first-line therapy for unresectable RAS and BRAF wild type metastatic colorectal cancer (mCRC). Older patients may not be suitable for combination regimens. We investigated 2 lower intensity pan-containing regimens.

Dispersion-free inertial focusing (DIF) for high-yield polydisperse micro-particle filtration and analysis.

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF).

Estimating survival scenarios in advanced or metastatic gastric and oesophageal adenocarcinoma: a systematic review of randomized-controlled trials.

Background: We aimed to summarize survival data from RCTs in patients with GO adenocarcinoma; estimate and explain worst-, typical-, and best-case-scenarios of survival time; and determine if simple multiples of median overall survival (mOS) could estimate these percentiles.

Methods: We systematically searched RCTs of systemic therapies for GO adenocarcinoma published 2000-2022. The following key percentiles were extracted from overall survival curves: 90th (worst-case), 75th (lower-typical), 25th (upper-typical), and 10th (best-case).

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation.