COVID-19

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

RATIONALE AND OBJECTIVES: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in pati...

Artificial neural network-driven modeling of Ebola transmission dynamics with delays and disability outcomes.

This study develops an Artificial Neural Network (ANN)-based framework to model the transmission dynamics and long-term disability outcomes of Ebola V...

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 ...

Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the S2 Step.

Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their abilit...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.

Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in det...

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.

PURPOSE: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.

Predicting purification process fit of monoclonal antibodies using machine learning.

In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensi...

Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficienci...

Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.

To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related disc...

Productive biosensing techniques empowered by all-dielectric metasurfaces.

Artificially designed, functional nanostructured surfaces, called metasurfaces, are an emerging platform for biosensing. Two major types of metasurfac...

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these...

Acoustic COVID-19 Detection Using Multiple Instance Learning.

In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnost...

A narrative review focusing on randomized clinical trials of vitamin D supplementation for COVID-19 disease.

Current evidence is inconsistent on whether vitamin D supplementation can prevent COVID-19 infection or improve its clinical outcomes. To better under...

A hierarchy category of socially assistive robots' functions: Insights from older adults.

Socially assistive robots (SARs) are increasingly recognized for their potential in helping older adults age in place. Effectively meeting the diverse...

DFCL: Dual-pathway fusion contrastive learning for blind single-image visible watermark removal.

Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright prote...

Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.

OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inh...

Real-time isolation of physiological tremor using recursive singular spectrum analysis and random vector functional link for surgical robotics.

Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms i...

Managing emergency crises using secure information through educational awareness: COVID-19 case study.

Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especi...