Prognostic value of adjuvant chemotherapy for hormone receptor-negative T1a and T1bN0M0 breast cancer patients.

The benefit of adjuvant chemotherapy (CT) for hormone receptor-negative T1a and T1bN0M0 breast cancer remains uncertain. Our study was to explore prognostic value and identify candidates of adjuvant CT for these patients. The data of hormone receptor-negative T1a and T1bN0M0 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015.

Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis.

Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.

Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics.

Bone is a multifaceted tissue requiring orchestrated interplays of diverse cells within specialized microenvironments. Although significant progress has been made in understanding cellular and molecular mechanisms of component cells of bone, revealing their spatial organization and interactions in native bone tissue microenvironment is crucial for advancing precision medicine, as they govern fundamental signaling pathways and functional dependencies among various bone cells. In this study, we present the first integrative high-resolution map of human bone and bone marrow, using spatial and single-cell transcriptomics profiling from femoral tissue.

A spherical vector-based adaptive evolutionary particle swarm optimization for UAV path planning under threat conditions.

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions.

Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal carcinoma of the breast.

Whether primary lesion surgery improves survival in patients with de novo metastatic breast cancer (dnMBC) is inconclusive. We aimed to establish a prognostic prediction model for patients with de novo metastatic breast invasive ductal carcinoma (dnMBIDC) based on machine learning algorithms and to investigate the value of primary site surgery. The data used in our study were obtained from the Surveillance, Epidemiology, and End Results database (SEER, 2010-2021) and the First Affiliated Hospital of Nanchang University (1st-NCUH, June 2013-June 2023).