Covalent organic frameworks and metal-organic frameworks against pathogenic viruses and antibiotic-resistant bacteria: Diagnostic and therapeutic applications

Antimicrobial resistance and antiviral infections statistics show that the number of global cases has been exponentially increasing;thus there is an unmet need for developing alternatives rather than to continue conventional strategies such as antibiotic administration, since they failed to show promise especially during the past few decades. Among different porous materials, metal-organic frameworks (MOFs) are a class of porous coordination polymers broadly explored in nano- and biomedicine due to their desirable properties, including excellent surface area, structural variability, the richness of their crystal structures/architectures, allowing for engineering synergies between metal nodes, functional linkers, encapsulated substrates or nanoparticles, heterogeneous catalysis, ion exchange, controlled and targeted drug delivery, energetics, etc. MOF-based sensing platforms have shown suitable potentials for specific viral detection. Covalent organic frameworks (COFs) are porous crystalline organic materials with two- or three-dimensional structures, which can be employed for reducing the interaction between the spike protein of SARS-CoV-2 and angiotensin-converting enzyme 2 (ACE2) in addition to other inhibitory effects. These frameworks can be applied for encapsulating antibiotics or antiviral agents against pathogens;they have been also studied for photodynamic inactivation of pathogenic bacteria. Herein, the most recent advancements pertaining to the applications of these frameworks for specific detection and inhibition of pathogenic viruses and antibiotic-resistant bacteria are cogitated, focusing on important challenges and perspectives. This review also provides expert recommendations on the future development and utility of these frameworks to manage antimicrobial resistance and infectious diseases more efficiently. © 2023 Elsevier Ltd

Scheelite-type rare earth vanadates TVO4 (T = Ho, Y, Dy) electrocatalysts: Investigation and comparison of T site variations towards bifunctional electrochemical sensing application

In the wake of the recent COVID-19 pandemic, antibiotics are now being used in unprecedented quantities across the globe, raising major concerns regarding pharmaceutical pollution and antimicrobial resistance (AMR). In view of the incoming tide of alarming apprehensions regarding their aftermath, it is critical to investigate control strategies that can halt their spread. Rare earth vanadates notable for their fundamental and technological significance are increasingly being used as electrochemical probes for the precise quantification of various pharmaceutical compounds. However, a comprehensive study of the role of the cationic site in tailoring the response mechanism is relatively unexplored. Hence, in this work we present a facile hydrothermal synthesis route of rare earth vanadates TVO4 (T = Ho, Y, Dy) as efficient electrocatalyst for the simultaneous detection of nitrofurazone (NF) and roxarsone (RX). There appears to be a significant correlation between T site substitution, morphological and the electrochemical properties of rare earth metal based vanadates. Following a comparative study of the electrochemical activity, the three rare-earth vanadates were found to respond differently depending on their composition of T sites. The results demonstrate that Dy-based vanadate displays increased electrical conductivity and rapid charge transfer characteristics. Thus, under optimal reaction conditions DyVO4- based electrodes imparts outstanding selectivity towards the detection of NF and RX with an extensive detection window of NF = 0.01–264 µM & RX = 0.01–21 µM and 36–264 µM and low detection limit (0.002, 0.0009 µM for NF and RX, respectively). In real-time samples, the proposed sensor reveals itself to be a reliable electrode material capable of detecting residues such as NF and RX. © 2022 Elsevier B.V.

Machine Learning: A promising in-silico approach to curb antimicrobial resistance

Antimicrobial resistance (AMR) is a concern to public health, prompting the development of novel strategies for combating AMR. While the use of machine learning (ML) to AMR is in its infancy, it has made significant progress as a diagnosis tool, owing to the growing availability of phenotypic/genotypic datasets and much faster computational power. While applying ML in AMR research is viable, its use is limited. It has been used to predict antimicrobial susceptibility genotypes/phenotypes, discover novel antibiotics, and improve diagnosis when combined with spectroscopic and microscopy methods. ML implementation in healthcare settings has challenges to adoption due to concerns about model interpretability and data integrity. The focus of this review is to outline the significant benefits and drawbacks along with the salient trends reported in recent studies. © 2022 IEEE.

Infection Prevention and Control Using Self Sterlizing Door Handle for Covid

Quality health care is mainly influenced by Infection Prevention and Control (IPC). Healthcare facilities and general public during this covid crisis is very much in need of IPC procedures. Due to this emerging covid crisis the IPC has implemented several measures to stop the spread of pathogens and other transmissible diseases. This paper is precisely focused on the goal of maintaining a safe and healthier environment. Many solutions has been proposed during the covid crisis but it is proven that UV-C light rays has an notable effect on sterilizing the infected areas and it avoids the spread of pathogens, other than tedious methods, UV-C lights on door handles is and effective method in which they are activated when it is in a rest position and disinfects the handle by, killing pathogens. Among the UV spectrum, UVC lights are used in those handles to prevent infections which is the most effective of all the categories of UV light. As the hand to UV rays contact is very less, exposure while touching the handle, its effects on the skin is almost down to zero, non-existent. This device is highly efficient and can be used to reduce the contact ratio with the pathogens. © 2021 IEEE.

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations. Author

One Health and human health

The "One Health" concept emphasizes interdisciplinary, cross-sectoral, and cross-regional communication and cooperation to achieve unity of health for humans, animals, and the environment. The One Health strategy is designed to provide early warning and effective preventative monitoring for emerging and reemerging infectious diseases. Since the avian flu crisis in the Asia-Pacific region, the World Organization for Animal Health and the World Health Organization have been working together to provide strong leadership to endorse the One Health concept and promote interagency and intersectoral collaboration. While the COVID-19 epidemic has been a major disaster for mankind, it has also resulted in a broad consensus on the concept of One Health among governments and international organizations, emphasizing action to jointly deal with common problems facing human health. Climate change, fragmentation and pollution of habitats, and the consequent loss of biodiversity and degradation of the natural environment threaten Earth's ecosystems. These changes also drive the emergence of infectious diseases, with negative health outcomes for humans, animals, and the environment. Historically, interventions in human and agricultural health problems did not always consider wildlife or environmental health, which has led to unintended consequences. One Health recognizes the interdependence of humans, animals, and the environment, and provides a conceptual framework for the development of interventions to optimize outcomes for human-animal-environmental health. However, it is necessary to clearly articulate the core values, goals, and objectives of One Health for all relevant sectors to maximize synergies in communication, coordination, collaboration, and, ultimately, joint action on disease control and prevention. The application of systems and harm reduction methods, focusing on the socio-economic and environmental determinants of health and ensuring good governance and effective leadership, will also maximize the opportunities to create "win-win" solutions for global health and environmental challenges. These solutions will help drive One Health to achieve its full potential and optimize health outcomes for all. In recent decades, One Health has become increasingly recognized around the world-i.e., that supporting a multisectoral, collaborative One Health strategy is the best way to address health threats at the human-animal-environmental level. The One Health method is increasingly popular in the context of the growing threats of climate change, emerging zoonoses, and antimicrobial drug resistance. During the last decade, country after country has implemented the One Health method and has shown benefits;the concept of One Health has become the international standard for zoonotic disease control. This call for transdisciplinary collaboration among professionals in human, animal, and environmental health has achieved multiple successes in zoonotic disease control, surveillance, and research. This article gives an overview of the development and application of One Health in addressing current issues, including emerging infectious diseases, antibiotic resistance, environmental health, and foodborne diseases. © 2022, Science Press. All right reserved.

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.