COVID-19

Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images.

Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the ...

Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis.

PURPOSE: Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia on CT of the chest, these results have not been collectively evaluated. We performed a meta-analysis of original articles comparing the performance of DL models versus radiologists in detecting COVID-19 pneumonia.

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning.

Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and envir...

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optim...

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.

The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventi...

"KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primari...

Diagnostic Challenges during Inflammation and Cancer: Current Biomarkers and Future Perspectives in Navigating through the Minefield of Reactive versus Dysplastic and Cancerous Lesions in the Digestive System.

In the setting of pronounced inflammation, changes in the epithelium may overlap with neoplasia, often rendering it impossible to establish a diagnosi...

Chemical composition, antioxidant, and antimicrobial properties of : in sight and analysis.

Our research focused on assessing essential oils (MSEO) and aqueous extracts (MSAE) derived from M. subtomentella leaves, with a primary focus on eval...

Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis.

Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mec...

ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.

Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current clinical diagnosis is accomplished using behavioral, cognitive...

Development and application of an amplified luminescent proximity homogeneous assay-linked immunosorbent assay for the accurate quantification of kidney injury molecule-1.

Kidney injury molecule-1 (Kim-1), a specific marker of kidney injury, is usually not expressed in normal kidneys or at very low levels but is highly ...

Effect of Oregon grape root extracts on P-glycoprotein mediated transport in cell lines.

This study aims to investigate the potential of Oregon grape root extracts to modulate the activity of P-glycoprotein. We performed H-CsA or H-digox...

Nondestructive estimation method of live chicken leg weight based on deep learning.

In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing tec...

Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.

Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, i...

Individual honey bee tracking in a beehive environment using deep learning and Kalman filter.

The honey bee is the most essential pollinator and a key contributor to the natural ecosystem. There are numerous ways for thousands of bees in a hive...

A novel ventriculoperitoneal shunt flow sensor based on electrically induced spatial variation in cerebrospinal fluid charge density.

Ventriculoperitoneal (VP) shunts divert cerebrospinal fluid (CSF) out of cerebral ventricles in patients with hydrocephalus or elevated intracranial ...

Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.

MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study...

Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method.

Pathogenic variation in DNA mismatch repair (MMR) gene is associated with Lynch syndrome (LS), an autosomal dominant hereditary cancer. Of the 3798 ...

Pharmacokinetics of recombinant human annexin A5 (SY-005) in patients with severe COVID-19.

Annexin A5 is a phosphatidylserine binding protein with anti-inflammatory, anticoagulant and anti-apoptotic properties. Preclinical studies have show...

Using real-world data to inform dosing strategies of rituximab for pediatric patients with frequent-relapsing or steroid-dependent nephrotic syndrome: a prospective pharmacokinetic-pharmacodynamic study.

Rituximab is frequently used off-label for the treatment of frequent-relapsing/steroid-dependent nephrotic syndrome (FRNS/SDNS). However, the optimal...