Smartphone-based diagnostics for biosensing infectious human pathogens.

The widespread usage of smartphones has made accessing vast troves of data easier for everyone. Smartphones are powerful, handy, and easy to operate, making them a valuable tool for improving public health through diagnostics. When combined with other devices and sensors, smartphones have shown potential for detecting, visualizing, collecting, and transferring data, enabling rapid disease diagnosis. In resource-limited settings, the user-friendly operating system of smartphones allows them to function as a point-of-care platform for healthcare and disease diagnosis. Herein, we critically reviewed the smartphone-based biosensors for the diagnosis and detection of diseases caused by infectious human pathogens, such as deadly viruses, bacteria, and fungi. These biosensors use several analytical sensing methods, including microscopic imaging, instrumental interface, colorimetric, fluorescence, and electrochemical biosensors. We have discussed the diverse diagnosis strategies and analytical performances of smartphone-based detection systems in identifying infectious human pathogens, along with future perspectives.

Mapping human pathogens in wastewater using a metatranscriptomic approach.

The monitoring of cities' wastewaters for the detection of potentially pathogenic viruses and bacteria has been considered a priority during the COVID-19 pandemic to monitor public health in urban environments. The methodological approaches frequently used for this purpose include deoxyribonucleic acid (DNA)/Ribonucleic acid (RNA) isolation followed by quantitative polymerase chain reaction (qPCR) and reverse transcription (RT)‒qPCR targeting pathogenic genes. More recently, the application of metatranscriptomic has opened opportunities to develop broad pathogenic monitoring workflows covering the entire pathogenic community within the sample. Nevertheless, the high amount of data generated in the process requires an appropriate analysis to detect the pathogenic community from the entire dataset. Here, an implementation of a bioinformatic workflow was developed to produce a map of the detected pathogenic bacteria and viruses in wastewater samples by analysing metatranscriptomic data. The main objectives of this work was the development of a computational methodology that can accurately detect both human pathogenic virus and bacteria in wastewater samples. This workflow can be easily reproducible with open-source software and uses efficient computational resources. The results showed that the used algorithms can predict potential human pathogens presence in the tested samples and that active forms of both bacteria and virus can be identified. By comparing the computational method implemented in this study to other state-of-the-art workflows, the implementation analysis was faster, while providing higher accuracy and sensitivity. Considering these results, the processes and methods to monitor wastewater for potential human pathogens can become faster and more accurate. The proposed workflow is available at https://github.com/waterpt/watermonitor and can be implemented in currently wastewater monitoring programs to ascertain the presence of potential human pathogenic species.

Effects of bio-contaminants in organic waste products on the soil environment

Nowadays, organic wastes stemming from schools, hospitals, restaurants, workplaces, markets, residences, and wastewater treatment plants are becoming a complex phenomenon, attracting the attention of scientists to convert them into valuable products that can be used to improve soil quality. However, land application of organic waste products (OWPs) is a significant pathway for spreading bio-contaminants such as viruses, antibiotic resistance genes (ARGs), and human pathogenic bacteria (HPB) in the soil environment, which poses a significant concern for the health of human beings if they move into the food chain. In this chapter, the knowledge focused on the effects of bio-contaminants in the products recycled from organic wastes on the soil environment. Herein, the information regarding the types and risks of viruses, ARGs, and HPB in different organic wastes and in different soil regions was substantially introduced, and the emerging bio-contaminants such as SARS-CoV-2 and ARGs were also presented. Moreover, the pollution pathway from OWPs to the soil was detailed. This chapter demonstrates the types of viruses, ARGs, and HPB, soil contamination processes, perspectives, and mitigation measures. This can be used as a guide for protecting human beings when dealing with organic waste. © 2023 Elsevier Inc. All rights reserved.

The pit latrine paradox in low-income settings: A sanitation technology of choice or a pollution hotspot?

Pit latrines are widely promoted to improve sanitation in low-income settings, but their pollution and health risks receive cursory attention. The present narrative review presents the pit latrine paradox; (1) the pit latrine is considered a sanitation technology of choice to safeguard human health, and (2) conversely, pit latrines are pollution and health risk hotspots. Evidence shows that the pit latrine is a 'catch-all' receptacle for household disposal of hazardous waste, including; (1) medical wastes (COVID-19 PPE, pharmaceuticals, placenta, used condoms), (2) pesticides and pesticide containers, (3) menstrual hygiene wastes (e.g., sanitary pads), and (4) electronic wastes (batteries). Pit latrines serve as hotspot reservoirs that receive, harbour, and then transmit the following into the environment; (1) conventional contaminants (nitrates, phosphates, pesticides), (2) emerging contaminants (pharmaceuticals and personal care products, antibiotic resistance), and (3) indicator organisms, and human bacterial and viral pathogens, and disease vectors (rodents, houseflies, bats). As greenhouse gas emission hotspots, pit latrines contribute 3.3 to 9.4 Tg/year of methane, but this could be an under-estimation. Contaminants in pit latrines may migrate into surface water, and groundwater systems serving as drinking water sources and pose human health risks. In turn, this culminates into the pit latrine-groundwater-human continuum or connectivity, mediated via water and contaminant migration. Human health risks of pit latrines, a critique of current evidence, and current and emerging mitigation measures are presented, including isolation distance, hydraulic liners/ barriers, ecological sanitation, and the concept of a circular bioeconomy. Finally, future research directions on the epidemiology and fate of contaminants in pit latrines are presented. The pit latrine paradox is not meant to downplay pit latrines' role or promote open defaecation. Rather, it seeks to stimulate discussion and research to refine the technology to enhance its functionality while mitigating pollution and health risks.

The peptide link between pathogens and human centromere proteins

This work tested the hypothesis that infection causes unexplained production of anti-centromere protein antibodies (ACA) via autoimmune cross-reactivity. To further examine the clinical origin of ACA, the overlapped peptides between human pathogens, including viruses, bacteria and fungi and centromere proteins (CENP-A, CENP-B and CENP-C) were assessed. We found a broad overlap of pathogenetic peptides with human centromere proteins. These data indicate potential immune cross-reactivity between pathogens and human centromere proteins. Additionally, the current findings corroborate a molecular and mechanistic framework for autoimmune disorders related to infection. Moreover, preliminary evidence for a potential role of infection in ACA-related autoimmune diseases was presented. © 2022 The Scandinavian Foundation for Immunology.

Zoonotic Coronaviruses Regeneration of Capto™ Core 700 resin through high throughput and laboratory scale studies and impact on production of a SARS-CoV-2 vaccine candidate

Summary Coronaviruses (CoVs) are important pathogens for humans and vertebrate animals. These viruses can infect the respiratory, gastrointestinal, hepatic and central nervous system of humans, livestock, birds, bats and other wild animals. The CoVs have frequently crossed species barriers and some have emerged as important human pathogens. Bats are well adapted anatomically and physiologically to CoVs. During the twenty-first century, the world has witnessed two pandemics of coronaviruses (SARSCoV and SARS-CoV-2) and fatal sporadic outbreaks due to the emerging Middle East respiratory syndrome-CoV. Most of the coronaviruses are transmitted through respiratory droplets during coughing or sneezing. Transmission through the ocular surface is also possible. Animal CoVs have been known since the late 1930s. Various diagnostic tests are available for the detection of coronaviruses infection which is mainly based on antigen detection or specific antibody detection. During the development of a SARS-CoV-2 vaccine candidate, at the height of the COVID-19 pandemic, raw materials shortages, including chromatography resins, necessitated the determination of a cleaning in place (CIP) strategy for a multimodal core-shell resin both rapidly and efficiently. Here, we describe the deployment of high throughput (HT) techniques to screen CIP conditions for cleaning Capto? Core 700 resin exposed to clarified cell culture harvest of a SARS-CoV-2 vaccine candidate produced in Vero adherent cell culture. The best performing conditions, comprised of 30% n-propanol and ≥0.75 N NaOH, were deployed in cycling experiments, completed with miniature chromatography columns, to demonstrate their effectiveness. The success of the CIP strategy was ultimately verified at the laboratory scale. Here, its impact was assessed across the entire purification process which also included an ultrafiltration/diafiltration step. It is shown that the implementation of the CIP strategy enabled the re-use of the Capto Core 700 resin for up to ten cycles without any negative impact on the purified product. Hence, the strategic combination of HT and laboratory-scale experiments can lead rapidly to robust CIP procedures, even for a challenging to clean resin, and thus help to overcome supply shortages. This article is protected by copyright. All rights reserved

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach.

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes.

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.