Infectious Disease

Use of pressure-regulated volume control in the first 48 hours of hospitalization of mechanically ventilated patients with sepsis or septic shock, with or without ARDS.

PURPOSE: To evaluate the impact of pressure-regulated volume control (PRVC/VC+) use on delivered tidal volumes in patients with acute respiratory distress syndrome (ARDS) or at risk for ARDS.

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.

Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different cellular and molecular components are involved. Of note is that the previously identified correlates of protection fail to account for the majority o...

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of vari...

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional me...

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tub...

A SEMIPARAMETRIC MODELING APPROACH USING BAYESIAN ADDITIVE REGRESSION TREES WITH AN APPLICATION TO EVALUATE HETEROGENEOUS TREATMENT EFFECTS.

Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariat...

Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania.

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this in...

Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.

BACKGROUND: The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine lea...

Screening physicochemical, microbiological and bioactive properties of fruit vinegars produced from various raw materials.

In the present study, variety of fruit vinegars were investigated in terms of their physicochemical, microbiological and bioactive properties. Total p...

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused ...

Sepsis in Latent Autoimmune Diabetes in Adults with Diabetic Ketoacidosis: A Case Report.

BACKGROUND: This case report intends to highlight the challenge in diagnosing type 1 diabetes on an adult patient. Latent Autoimmune Diabetes in Adult...

Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 in...

Compendiums of cancer transcriptomes for machine learning applications.

There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing...

Clinical applications of artificial intelligence in sepsis: A narrative review.

Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the l...

Exploring single-cell data with deep multitasking neural networks.

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and...

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

BACKGROUND: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for ...

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning.

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analy...

Effect of storage on the quality of processed palm oil collected from local milling points within Ile-Ife, Osun State, Nigeria.

The influence of storage practices on physicochemical and microbial changes in crude palm oil (CPO) from milling points in Ile-Ife, Nigeria were inves...

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the presc...

Non-faradaic electrochemical impedimetric profiling of procalcitonin and C-reactive protein as a dual marker biosensor for early sepsis detection.

In this work, we demonstrate a robust, dual marker, biosensing strategy for specific and sensitive electrochemical response of Procalcitonin and C-rea...