Viral infections are a significant public health problem, primarily due to their high transmission rate, various pathological manifestations, ranging from mild to severe symptoms and subclinical onset. Laboratory diagnostic tests for infectious diseases, with a short enough turnaround time, are promising tools to improve patient care, antiviral therapeutic decisions, and infection prevention. Numerous microbiological molecular and serological diagnostic testing devices have been developed and authorised as benchtop systems, and only a few as rapid miniaturised, fully automated, portable digital platforms. Their successful implementation in virology relies on their performance and impact on patient management. This review describes the current progress and perspectives in developing micro- and nanotechnology-based solutions for rapidly detecting human viral respiratory infectious diseases. It provides a nonexhaustive overview of currently commercially available and under-study diagnostic testing methods and discusses the sampling and viral genetic trends as preanalytical components influencing the results. We describe the clinical performance of tests, focusing on alternatives such as microfluidics-, biosensors-, Internet-of-Things (IoT)-based devices for rapid and accurate viral loads and immunological responses detection. The conclusions highlight the potential impact of the newly developed devices on laboratory diagnostic and clinical outcomes.

Antimicrobial and anticancer drug resistance represent two of the main global challenges for the public health, requiring immediate practical solutions. In line with this, we need a better understanding of the origins of drug resistance in prokaryotic and eukaryotic cells and the evolutionary processes leading to the occurrence of adaptive phenotypes in response to the selective pressure of therapeutic agents. The purpose of this paper is to present some of the analogies between the antimicrobial and anticancer drug resistance. Antimicrobial and anticancer drugs share common targets and mechanisms of action as well as similar mechanisms of resistance (e.g., increased drug efflux, drug inactivation, target alteration, persister cells’ selection, protection of bacterial communities/malignant tissue by an extracellular matrix, etc.). Both individual and collective stress responses triggered by the chemotherapeutic agent involving complex intercellular communication processes, as well as with the surrounding microenvironment, will be considered. The common themes in antimicrobial and anticancer drug resistance recommend the utility of bacterial experimental models for unraveling the mechanisms that facilitate the evolution and adaptation of malignant cells to antineoplastic drugs.

Cancer is a very aggressive disease and one of mankind’s most important health problems, causing numerous deaths each year. Its etiology is complex, including genetic, gender-related, infectious diseases, dysbiosis, immunological imbalances, lifestyle, including dietary factors, pollution etc. Cancer patients also become immunosuppressed, frequently as side effects of chemotherapy and radiotherapy, and prone to infections, which further promote the proliferation of tumor cells. In recent decades, the role and importance of the microbiota in cancer has become a hot spot in human biology research, bringing together oncology and human microbiology. In addition to their roles in the etiology of different cancers, microorganisms interact with tumor cells and may be involved in modulating their response to treatment and in the toxicity of anti-tumor therapies. In this review, we present an update on the roles of microbiota in cancer with a focus on interference with anticancer treatments and anticancer potential.

Metabolic syndrome (MetSyn) has a rapidly growing worldwide prevalence, affecting over 1 billion people. MetSyn is clustering many pathological conditions, which, untreated, could increase the risk and often lead to more severe metabolic defects such as type 2 diabetes and non-alcoholic fatty liver disease. Many data demonstrate the complex role of gut microbiota in the host metabolism, and hence, deciphering the microbiome patterns linked to MetSyn could enable us for novel diagnosis and monitoring markers and for better disease management. Moreover, interventions designed to alter patient microbiome composition may help prevent or decrease morbidity linked with MetSyn. However, the microbiome composition is largely different across geographically distinct populations. Our study investigated the microbiota and mycobiome patterns in Romanian metabolic syndrome patients. We also correlated the identified microbiome–mycobiome patterns with levels of metabolites important for host health such as short chain fatty acids, organic acids, and taurine. We found that MetSyn patients are harboring a microbiome enriched in Enterobacteriaceae, Turicibacter sp., Clostridium coccoides, and Clostridium leptum, while beneficial taxa such as Butyricicoccus sp., Akkermansia muciniphila, and Faecalibacterium prausnitzii were decreased. These microbiome changes were correlated with lower butyrate levels and increased succinate. In terms of mycobiome signatures, MetSyn was associated with a high abundance of Saccharomyces and Aspergillus species. Our data are the first reported on a Romanian population and confirming that the pathogenesis of MetSyn is closely related to gut microbiome and homeostasis.