AIMC Topic: Coinfection

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Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.

Scientific reports
This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN pati...

Cases of Monkeypox show highly-overlapping co-infection with HIV and syphilis.

Frontiers in public health
PURPOSE: Ongoing Monkeypox (MPX) outbreaks in countries outside Africa have unique characteristics. However, data on cohorts of confirmed cases in China is limited. The study provides important epidemiological, diagnostic, and clinical information ab...

Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.

Biochemical genetics
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coin...

Treating drug-resistant tuberculosis in an era of shorter regimens: Insights from rural South Africa.

South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
BACKGROUND: Progressive interventions have recently improved programmatic outcomes in drug-resistant tuberculosis (DR-TB) care in South Africa (SA). Amidst these, a shorter regimen was introduced in 2017 with weak evidence, and has shown mixed result...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Microbiology and immunology
Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes (MDGs) of host blood could be used to diagnose patients co-infected with HIV/TB....

A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Contrast media & molecular imaging
Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devasta...

PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses.

Scientific reports
Viral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning alg...

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals.

PloS one
Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our ob...

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who ...