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. Results showed significant reductions in total dissolved solids across most systems, with a decrease from 210 ± 23.6 mg/L in tap water to 21 ± 2.8 mg/L in filtered water for PoU-10. Ammonia nitrogen levels in tap water decreased from 0.05 ± 0.04 to 2.28 ± 1.47 mg/L to 0.02 ± 0.04 to 0.69 ± 0.64 mg/L in filtered water. Despite this, bacterial indicators showed no significant changes, with some systems even increasing coliform levels. Protozoological analysis identified prevalent Acanthamoeba (42.5%), less frequent Naegleria (2.5%), Vermamoeba vermiformis (5%), and potentially pathogenic Acanthamoeba genotypes. Elevated bacterial indicators in filtered water of point-of-use systems, combined with essential mineral removal, indicate non-compliance with water quality standards, posing a public health concern. Further research on the long-term health implications of these filtration systems is essential.

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. Baseline enrollment was done from August 2015 to March 2017. The study protocol was amended in April 2020 to allow COVID-19 incidence and seroprevalence studies. A total of 290 households including 1598 participants were enrolled and followed from April to October 2020 in four study sites. When a participant showed respiratory illness symptoms, a serum sample and a nasal and an oropharyngeal swab were obtained. Swabs were tested by RT-PCR for SARS-CoV-2 infection. If positive, the subject was followed and swabs collected on days three, six, nine, and 14 after the first swab day and a serum sample obtained on day 14. All subjects residing with the index case were swabbed following the same sampling schedule. Sera were collected from cohort participants in October 2020 to assess seroprevalence. Swabs were tested by RT-PCR. Sera were tested by Microneutralization Assay to measure the neutralizing antibody titer. Incidence of COVID-19, household secondary attack rate, and seroprevalence in the cohort were determined. The incidence of COVID-19 was 6.9% and the household secondary attack rate was 89.8%. Transmission within households occurred within two-days of confirming the index case. Infections were asymptomatic or mild with symptoms resolving within 10 days. The majority developed a neutralizing antibody titer by day 14 post onset. The overall seroprevalence among cohort participants was 34.8%. These results suggest that within-household transmission is high in Egypt. Asymptomatic or mild illness is common. Most infections seroconvert and have a durable neutralizing antibody titer.

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. We collected demographic, exposure, and health condition data and annual serum samples from each participant and obtained swab samples from participants reporting influenza-like illness symptoms. We performed serologic and molecular analyses and detected 4 cases of infection with H5N1 and 3 cases with H9N2. We detected very low seroprevalence of H5N1 antibodies and no H5N8 antibodies among the cohort; up to 11% had H9 antibodies. None of the exposure, health status, or demographic variables were related to being seropositive. Our findings indicate that avian influenza remains a public health risk in Eqypt, but infections may go undetected because of their mild or asymptomatic nature.

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. Choosing the right device with the desired working mode, signal quality, and the device cost are still the main obstacles to massive usage of these devices. METHODS: In this paper, we introduce design, implementation, and validation of a multi-purpose telemetry system for recording, transmission, and interpretation of ECG signals in different recording modes. The system consists of an ECG device, a cloud-based analysis pipeline, and accompanied mobile applications for physicians and patients. The proposed ECG device's mechanical design allows laypersons to easily record post-event short-term ECG signals, using dry electrodes without any preparation. Moreover, patients can use the device to record long-term signals in loop and holter modes, using wet electrodes. In order to overcome the problem of signal quality fluctuation due to using different electrodes types and different placements on subject's chest, customized ECG signal processing and interpretation pipeline is presented for each working mode. RESULTS: We present the evaluation of the novel short-term recorder design. Recording of an ECG signal was performed for 391 patients using a standard 12-leads golden standard ECG and the proposed patient-activated short-term post-event recorder. In the validation phase, a sample of validation signals followed peer review process wherein two experts annotated the signals in terms of signal acceptability for diagnosis.We found that 96% of signals allow detecting arrhythmia and other signal's abnormal changes. Additionally, we compared and presented the correlation coefficient and the automatic QRS delineation results of both short-term post-event recorder and 12-leads golden standard ECG recorder. CONCLUSIONS: The proposed multi-purpose ECG device allows physicians to choose the working mode of the same device according to the patient status. The proposed device was designed to allow patients to manage the technical requirements of both working modes. Post-event short-term ECG recording using the proposed design provide physicians reliable three ECG leads with direct symptom-rhythm correlation.

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. RESULTS: Numerous studies highlight potential labor, as well as diagnostic accuracy, benefits to the implementation of AI for slide-based and macroscopic digital image analyses. These range from Gram stain interpretation to categorization and quantitation of culture growth. CONCLUSIONS: Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages. More research efforts and US Food and Drug Administration clearance are still required to fully incorporate these AI applications into routine clinical laboratory practices.

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%. The purpose of this review was to highlight cholera challenges in Africa and the Middle East and explain the reasons for why this region is currently a fertile environment for cholera. We investigated cholera serology, epidemiology, and the geographical distribution of cholera in Africa and the Middle East in 2022 and 2023. We reviewed detection methods, such as rapid diagnostic tests (RDTs), and treatments, such as antibiotics and phage therapy. Finally, this review explored oral cholera vaccines (OCVs), and the vaccine shortage crisis. Methods: We carried out a systematic search in multiple databases, including PubMed, Web of Science, Google Scholar, Scopus, MEDLINE, and Embase, for studies on cholera using the following keywords: ((Cholera) OR (Vibrio cholera) and (Coronavirus) OR (COVID-19) OR (SARS-CoV2) OR (The Middle East) OR (Africa)). Results and Conclusions: Cholera outbreaks have increased dramatically, mainly in Africa and many Middle Eastern countries. The COVID-19 pandemic has reduced the attention devoted to cholera and disrupted diagnosis and treatment services, as well as vaccination initiatives. Most of the cholera cases in Africa and the Middle East were reported in Malawi and Syria, respectively, in 2022. RDTs are effective in the early detection of cholera epidemics, especially with limited advanced resources, which is the case in much of Africa. By offering both direct and indirect protection, expanding the use of OCV will significantly reduce the burden of current cholera outbreaks in Africa and the Middle East.

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 dH2O required 1 min of treatment, while ɸX174 reductions of ~ 2log10 and 4log10 were obtained after 10 min of CAP exposure in river water and wastewater samples, respectively. Similarly, after 10 min of CAP treatment, bacterial concentrations decreased by 3 log10 and 4 log10, in river and wastewater samples, respectively. In contrast, after 30 s of contact time, a 4 log10 reduction of bacteria was accomplished in dH2O. Complete removal of Acanthamoeba from dH2O was found after 30 min of CAP treatment, whereas it was not removed from surface water or wastewater at the same exposure time. Additionally, the approach successfully reduced TOC, and the degradation kinetics of TOC were represented by pseudo-first-order. CAP showed higher rates of TOC degradation in the final effluent of the wastewater treatment plant compared to surface water. The difference in CAP performance between river water and wastewater could be attributed to the bulk structure of humic acids in river water compared to small organic byproducts in the final effluent of WWTP. Overall, the findings reported here support the idea that CAP holds promise as a sustainable solution for controlling pathogens, removing organic water pollution, and integrating with traditional purification processes. Low-cost systems may advance CAP technology and increase its widespread use.

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. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow. CONCLUSIONS: Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.

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. METHODS: We conducted a cross-sectional serologic survey among 888 participants in Egypt. RESULTS: Neutralizing antibodies were detected in 30% of study volunteers. Age and educational level were associated with being seropositive as people older than 70 years and people with graduate degrees had lower seroprevalence. Self-reporting cases having COVID-19-related symptoms such as fever, malaise, headache, dyspnea, dry cough, chest pain, diarrhea, and loss of taste or smell were all associated with having antibodies. Fever and loss of taste or smell were strong predictors with odds ratios of 2.1 (95% confidence interval: 1.3-3.5) and 4.5 (95% confidence interval: 2.6-7.8), respectively. CONCLUSIONS: Our results can guide COVID-19 prevention and control policies and assist in determining the immunity level in some Egyptian communities.

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. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

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. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.