Sepsis

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational opti...

Rational design of stapled antimicrobial peptides.

The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action an...

Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.

To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairme...

Alternative additives associated in the feeding of laying hens: performance, biometrics, bone traits, and economic evaluation-an unsupervised machine learning approach.

Given the current bans on the use of some growth promoting antibiotics in poultry nutrition, the need to use alternative additives which could replace...

Recognizing and monitoring infectious sources of schistosomiasis by developing deep learning models with high-resolution remote sensing images.

BACKGROUND: China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of i...

Efficient Evolving Deep Ensemble Medical Image Captioning Network.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increase...

Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

BACKGROUND: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compare...

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devic...

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections.

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though...

MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans.

The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19) lesions from computerized tomography (CT) scans are of great sig...

Coronavirus covid-19 detection by means of explainable deep learning.

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of Decem...

Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality.

It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although ...

Newest perspectives of glycopeptide antibiotics: biosynthetic cascades, novel derivatives, and new appealing antimicrobial applications.

Glycopeptide antibiotics (GPAs) are a family of non-ribosomal peptide natural products with polypeptide skeleton characteristics, which are considered...

Database-based machine learning in sepsis deserves attention.

Robot-assisted in situ bioprinting of gelatin methacrylate hydrogels with stem cells induces hair follicle-inclusive skin regeneration.

Large skin defects caused by accidents or disease can cause fluid loss, water and electrolyte disorders, hypoproteinemia and serious infection and rem...

OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection.

Sepsis is a major public concern due to its high mortality, morbidity, and financial cost. There are many existing works of early sepsis prediction us...

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models.

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and eco...

Minimally Invasive Lower Anterior Resections - Better than Open But Not All the Same.

Optimal approach for lower anterior resection has been closely debated. The relatively new addition of the robotic approach adds a layer of complexit...

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification.

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease contr...

Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affec...