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Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.

Frontiers in immunology
INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable...

Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Histologic assessment of the immune infiltrate in H&E slides is vital in diagnosing and managing inflammatory bowel diseases, but these assessments are subjective and time-consuming even for those with expertise. The develop...

Deep Learning-Based Blood Abnormalities Detection as a Tool for VEXAS Syndrome Screening.

International journal of laboratory hematology
INTRODUCTION: VEXAS is a syndrome described in 2020, caused by mutations of the UBA1 gene, and displaying a large pleomorphic array of clinical and hematological features. Nevertheless, these criteria lack significance to discriminate VEXAS from othe...

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis.

Scientific reports
Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increas...

Application of machine learning techniques in the diagnosis of endometriosis.

BMC women's health
OBJECTIVE: The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM).

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

International journal of molecular sciences
The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains in...

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

The clinical respiratory journal
BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict...

Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning.

BMC cancer
BACKGROUND: Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in ...

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the nee...