Building a Risk Scoring Model for ARDS in Lung Adenocarcinoma Patients Using Machine Learning Algorithms.

Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS), which increases mortality risks. Investigating the prognostic implications of ARDS-related genes in LUAD is crucial for improving clinical outcomes. Data from TCGA, GEO and GTEx were used to identify 276 ARDS-related genes in LUAD via differential expression analysis.

Engineered Shellac Beads-on-the-String Fibers Using Triaxial Electrospinning for Improved Colon-Targeted Drug Delivery.

Colon-targeted drug delivery is gradually attracting attention because it can effectively treat colon diseases. Furthermore, electrospun fibers have great potential application value in the field of drug delivery because of their unique external shape and internal structure. In this study, a core layer of hydrophilic polyethylene oxide (PEO) and the anti-colon-cancer drug curcumin (CUR), a middle layer of ethanol, and a sheath layer of the natural pH-sensitive biomaterial shellac were used in a modified triaxial electrospinning process to prepare beads-on-the-string (BOTS) microfibers.

Estimation of a Human-Maneuvered Target Incorporating Human Intention.

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states.