Article
Immunology
Camila Fonseca Rizek, Roberta Cristina Martins, Evelyne Santana Girao, Bruno de Melo Tavares, Sania Alves Dos Santos, Gessica Lorena Gamarra, Lauro Vieira Perdigao Neto, Constancia Diogo, Tatiana D' Annibale Orsi, Icaro Boszczowski, Filipe Piastrelli, Cecilia Leite Costa, Daniely Viana Costa, Geovania Maciel, Janete Romao, Gerly Anne de Castro Brito, Silvia Figueiredo Costa
Summary: Clostridioides difficile (CD) is the most common cause of healthcare-related diarrhea, and its severity has increased due to the spread of hypervirulent strains. Toxin production is the most important virulence factor for CD, but other factors, such as adhesion-related genes, also contribute to its virulence.
MICROBES AND INFECTION
(2022)
Article
Public, Environmental & Occupational Health
Junji Shiode, Masakuni Fujii, Junichiro Nasu, Mamoru Itoh, Shuhei Ishiyama, Akiko Fujiwara, Masao Yoshioka
Summary: A study investigated the changes in HO-CDI incidence after relocation to a new hospital and found a reduction in CO-CDI incidence associated with the move. Environmental improvements may have decreased the reservoir of C. difficile, leading to a decrease in asymptomatic carriers and CO-CDI patients.
AMERICAN JOURNAL OF INFECTION CONTROL
(2022)
Article
Infectious Diseases
Monique J. T. Crobach, Bastian V. H. Hornung, Cees Verduin, Margreet C. Vos, Joost Hopman, Nitin Kumar, Celine Harmanus, Ingrid Sanders, Elisabeth M. Terveer, Mark D. Stares, Trevor D. Lawley, Ed J. Kuijper
Summary: Screening for Clostridioides difficile colonization at admission did not detect any patients who progressed to symptomatic CDI, except for one possible transmission event. Therefore, screening for CDC at admission is not useful in this endemic setting.
CLINICAL MICROBIOLOGY AND INFECTION
(2023)
Article
Infectious Diseases
Nicholas Swart, Araadhna M. Sinha, Anthony Bentley, Helen Smethurst, Gordon Spencer, Sophia Ceder, Mark H. Wilcox
Summary: This study conducted a cost-utility analysis comparing the treatment pathways for Clostridioides difficile infection (CDI), and found that the treatment strategy recommended by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) guidelines was the most cost-effective from the perspective of the UK National Health Service (NHS).
CLINICAL MICROBIOLOGY AND INFECTION
(2023)
Article
Biochemistry & Molecular Biology
Fatemeh Eshari, Fahime Momeni, Amirreza Faraj Nezhadi, Soudabeh Shemehsavar, Mehran Habibi-Rezaei
Summary: A novel machine-learning approach based on logistic regression (LR) is used to predict protein aggregation propensity (PAP) using a dataset of hexapeptides and eight physiochemical features. The LR model, combined with sequence and feature information, achieves high accuracy and outperforms other existing methods in PAP prediction.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Gastroenterology & Hepatology
Etienne Nzabarushimana, Haixu Tang
Summary: CDI is a GI infection that can be reversed through modulation of the gut microbiota. This study evaluated the diagnostic capabilities of the fecal microbiome on CDI, showing that the species/function composition of the gut microbiome has a robust diagnostic prediction of the disease. The impact of antibiotic therapy on CDI prediction was also assessed, with positive outcomes observed following successful FMT.
Article
Medicine, General & Internal
Pengfei Fu, Yi Zhang, Jun Zhang, Jin Hu, Yirui Sun
Summary: An optimal prediction model can be generated to predict intracranial infection associated with external ventricular drainage, aiding in the prevention and treatment of this complication in neurointensive care units.
JOURNAL OF CLINICAL MEDICINE
(2022)
Article
Biochemistry & Molecular Biology
Hsiao-Yun Chao, Chin-Chieh Wu, Avichandra Singh, Andrew Shedd, Jon Wolfshohl, Eric H. Chou, Yhu-Chering Huang, Kuan-Fu Chen
Summary: This study developed and validated a prediction model for 28-day mortality in patients with infection using machine learning algorithms. The results showed that the random forest model outperformed other models and logistic regression in predicting mortality.
Article
Computer Science, Information Systems
Manuel Jimenez-Lazaro, Juan Luis Herrera, Javier Berrocal, Jaime Galan-Jimenez
Summary: This paper proposes a machine learning solution to predict energy-efficient network configurations in SDN, without the need for optimal or heuristic solutions, resulting in significantly reduced computation time.
Article
Microbiology
Johan Karp, Jon Edman-Waller, Michael Toepfer, Gunnar Jacobsson
Summary: The objectives of this study were to determine the risk factors for recurrent healthcare facility-associated Clostridioides difficile infection (HCF-CDI) in a high CDI incidence, low antibiotic use setting and to determine if length of cefotaxime exposure is a risk factor for recurrent HCF-CDI. The risk factors for recurrent HCF-CDI were evaluated through a retrospective nested case control study. Renal insufficiency and metronidazole treatment were identified as independent risk factors for recurrent HCF-CDI, while cefotaxime exposure showed a dose-dependent relationship with the risk of recurrence.
Article
Biology
Wojciech Ksiazek, Michal Gandor, Pawe Plawiak
Summary: This paper presents a novel diagnostics approach for hepatocellular carcinoma (HCC) using machine learning techniques, combining logistic regression with genetic algorithms. Through three experiments, the approach was shown to achieve a classification accuracy of 94.55% and an f1-score of 93.56%. The results indicate that machine learning techniques optimized by the proposed concept can provide a new and accurate approach in HCC diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Taqwa F. Shaban, Mahmoud Y. Alkawareek
Summary: Three optimized models based on machine learning were developed to predict in vitro antibiofilm activity of antibiotics, with factors such as minimum inhibitory concentration, bacterial Gram type, and biofilm formation method being significant predictors of prediction accuracy.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Allergy
Louise Cunningham, Clarisse Ganier, Felicity Ferguson, Ian R. White, Fiona M. Watt, John McFadden, Magnus D. Lynch
Summary: The study showed that the risk of a clinically relevant positive patch test can be predicted using clinical and demographic parameters. The gradient boosting method outperforms logistic regression in certain predictions, indicating the relevance of complex nonlinear interactions in risk prediction.
CONTACT DERMATITIS
(2022)
Article
Microbiology
K. Rainha, Debora Lins, R. F. Ferreira, C. L. Costa, B. Penna, B. T. Endres, K. W. Garey, R. M. C. P. Domingues, E. O. Ferreira
Summary: Clostridioides difficile has been identified as one of the primary causes of nosocomial diarrhea and pseudomembranous colitis in humans and other mammals after the use of broad-spectrum antibiotics. A case of C. difficile infection (CDI) in a 13-year-old male dog is described in Rio de Janeiro, Brazil.
Article
Multidisciplinary Sciences
Sara Dominguez-Rodriguez, Miquel Serna-Pascual, Andrea Oletto, Shaun Barnabas, Peter Zuidewind, Els Dobbels, Siva Danaviah, Osee Behuhuma, Maria Grazia Lain, Paula Vaz, Sheila Fernandez-Luis, Tacilta Nhampossa, Elisa Lopez-Varela, Kennedy Otwombe, Afaaf Liberty, Avy Violari, Almoustapha Issiaka Maiga, Paolo Rossi, Carlo Giaquinto, Louise Kuhn, Pablo Rojo, Alfredo Tagarro
Summary: Logistic regression is commonly used in medical predictions, but there are concerns about using machine learning algorithms with small sample sizes. This study compared the performance of 7 algorithms in predicting 1-year mortality and clinical progression in infants with HIV. Random Forest algorithm showed the highest accuracy, sensitivity, and AUC among the tested algorithms.