Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms.

Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I and IO (designated as I/IO in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters.

Functional and Radiological Outcome of Anterior Plate Stabilisation of the Sacroiliac Joint in Unstable Pelvic Injury.

Introduction: Sacroiliac joint disruption, resulting from high energy trauma can cause significant morbidity if no proper treatment given. Many techniques can be used to stabilise pelvic ring injuries. We studied the functional and radiological outcome following open reduction and anterior fixation of the sacroiliac joint and agreement between both outcomes.

Human umbilical cord mesenchymal stem cells derived-exosomes alleviate LPS-induced cervical inflammation and epithelial-mesenchymal transition.

Objective: Patients with chronic cervicitis are known to have an increased risk of infection with human papillomavirus (HPV), which is the primary cause of cervical cancer. Inhibition of cervical inflammation may reduce the risk of cervical cancer. This study investigated how human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-Ex) attenuated the lipopolysaccharide (LPS)-induced cervical inflammation.

Molecular characterization and risk analysis of Giardia duodenalis assemblages in corticosteroid-treated and non-treated patients in Ismailia, Arab Republic of Egypt.

Background: Giardia duodenalis (G. duodenalis) is an intestinal protozoan parasite of human and animal hosts. The present study investigated and compared the assemblages of G.

Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk.