Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis.

Objectives: This review summarizes the current and potential uses of artificial intelligence (AI) in the current state of clinical microbiology with a focus on replacement of labor-intensive tasks.

Methods: A search was conducted on PubMed using the key terms clinical microbiology and artificial intelligence. Studies were reviewed for relevance to clinical microbiology, current diagnostic techniques, and potential advantages of AI in routine microbiology workflows.

Commercial reverse osmosis point-of-use systems in Egypt failed to purify tap water.

This study addresses the heightened global reliance on point-of-use (PoU) systems driven by water quality concerns, ageing infrastructure, and urbanization. While widely used in Egypt, there is a lack of comprehensive evaluation of these systems. We assessed 10 reverse osmosis point-of-use systems, examining physicochemical, bacteriological, and protozoological aspects of tap water (inlets) and filtered water (outlets), adhering to standard methods for the examination of water and wastewater.

Cholera rages in Africa and the Middle East: A narrative review on challenges and solutions.

Background And Aim: Cholera is a life-threatening infectious disease that is still one of the most common acute watery diarrheal diseases in the world today. Acute diarrhea and severe dehydration brought on by cholera can cause hypovolemic shock, which can be fatal in minutes. Without competent clinical therapy, the rate of case fatality surpasses 50%.

Cold atmospheric plasma: a sustainable approach to inactivating viruses, bacteria, and protozoa with remediation of organic pollutants in river water and wastewater.

Innovative technologies are needed to enhance access to clean water and avoid waterborne diseases. We investigated the performance of cold atmospheric plasma (CAP), a clean and sustainable approach for microbial inactivation and total organic carbon (TOC) degradation in environmental water. Water matrices played a crucial role in the performance of CAP efficacy; for example, complete removal of ɸX174 from dHO required 1 min of treatment, while ɸX174 reductions of ~ 2log and 4log were obtained after 10 min of CAP exposure in river water and wastewater samples, respectively.

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.

Background: Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.

Results: Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset.

Determinants of having severe acute respiratory syndrome coronavirus 2 neutralizing antibodies in Egypt.

Background: Reported laboratory-confirmed COVID-19 cases underestimate the true burden of disease as cases without laboratory confirmation, and asymptomatic and mild cases are missed by local surveillance systems. Population-based seroprevalence studies can provide better estimates of burden of disease by taking into account infections that were missed by surveillance systems. Additionally, little is known about the determinants of seroconversion in community settings.

Incidence, household transmission, and neutralizing antibody seroprevalence of Coronavirus Disease 2019 in Egypt: Results of a community-based cohort.

SARS-CoV-2 virus is transmitted in closed settings to people in contact with COVID-19 patients such as healthcare workers and household contacts. However, household person-to-person transmission studies are limited. Households participating in an ongoing cohort study of influenza incidence and prevalence in rural Egypt were followed.

Deep learning-based cell composition analysis from tissue expression profiles.

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness.

Incidence and Seroprevalence of Avian Influenza in a Cohort of Backyard Poultry Growers, Egypt, August 2015-March 2019.

Currently enzootic avian influenza H5N1, H9N2, and H5N8 viruses were introduced into poultry in Egypt in 2006, 2011, and 2016, respectively. Infections with H5N1 and H9N2 were reported among poultry-exposed humans. We followed 2,402 persons from households raising backyard poultry from 5 villages in Egypt during August 2015-March 2019.

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data.

Multi-purpose ECG telemetry system.

Background: The Electrocardiogram ECG is one of the most important non-invasive tools for cardiac diseases diagnosis. Taking advantage of the developed telecommunication infrastructure, several approaches that address the development of telemetry cardiac devices were introduced recently. Telemetry ECG devices allow easy and fast ECG monitoring of patients with suspected cardiac issues.