Systematic review and meta-analysis of colistin plus meropenem therapy for the treatment of nosocomial pneumonia.

Background And Objectives: Nosocomial pneumonia caused by multidrug-resistant gram-negative bacteria presents a significant challenge for healthcare systems, as there are limited effective treatments available. This systematic review and meta-analysis aim to investigate the outcomes of colistin plus meropenem combination therapy on nosocomial pneumonia.

Materials And Methods: An exhaustive search of PubMed, Scopus, Web of Science (WOS), and Embase databases was conducted, resulting in the extraction of 5 studies for qualitative assessment and meta-analysis.

Alzheimer's disease: a comprehensive review of epidemiology, risk factors, symptoms diagnosis, management, caregiving, advanced treatments and associated challenges.

Background: Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired reasoning. It is the leading cause of dementia in older adults, marked by the pathological accumulation of amyloid-beta plaques and neurofibrillary tangles. These pathological changes lead to widespread neuronal damage, significantly impacting daily functioning and quality of life.

Modeling the time series of scorpion stings in Southwestern Iran.

Scorpion stings pose a significant public health concern in Iran, resulting in approximately 45,000-50,000 cases and 19 deaths annually. The Khuzestan and Hormozgan provinces have the highest reported incidence rates, with an estimated 36,000 cases each year. This study focused on modeling the time series data of scorpion stings, specifically in Shoushtar City, from 2017 to 2022.

The Alteration of Gut Microbiota Composition in Patients with Epilepsy: A Systematic Review and Meta-Analysis.

Background: There is accumulating evidence suggesting a connection between epilepsy, a neurologic disease with recurrent seizures, and gut microbiota. This systematic review and meta-analysis explores the alterations of GM composition in patients with epilepsy.

Methods: A systematic search was conducted up to June 26, 2024, across PubMed, Scopus, Web of Science, and Embase.

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.

Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.