DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.

Life stage impact on the human skin ecosystem: lipids and the microbial community.

Sebaceous free fatty acids are metabolized by multiple skin microbes into bioactive lipid mediators termed oxylipins. This study investigated correlations between skin oxylipins and microbes on the superficial skin of pre-pubescent children (N = 36) and adults (N = 100), including pre- (N = 25) and post-menopausal females (N = 25). Lipidomics and metagenomics revealed that Malassezia restricta positively correlated with the oxylipin 9,10-DiHOME on adult skin and negatively correlated with its precursor, 9,10-EpOME, on pre-pubescent skin.

Relationships between Chronic Cough and Asthma Control and Quality-of-Life in Patients with Severe Asthma: A 6-Month Longitudinal Analysis.

Background: The prevalence and clinical implications of chronic cough (CC) in patients with severe asthma receiving asthma treatment remain relatively unknown.

Objective: This study aimed to evaluate the relationships between CC and asthma control and quality-of-life (QoL) in patients with severe asthma through longitudinal analysis.

Methods: Baseline and 6-month follow-up data from the Korean Severe Asthma Registry were analyzed.

Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.

Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data.

Materials And Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively.

Cooking loss estimation of semispinalis capitis muscle of pork butt using a deep neural network on hyperspectral data.

This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts were used with pixel-based data augmentation. Principal component regression (PCR) and partial least squares regression (PLSR) models for predicting cooking loss in SC muscle showed higher R values with multiplicative signal correction, while the first derivative resulted in a lower root mean square error (RMSE).