SMARTgNOSTICS - Global Testing & Diagnostics Solutions for antimicrobial resistances

Henrique Barros

Principal Investigator

Integrated Member (PhD)

Type of project:


Proposing institution:


Participating institutions:

Instituto Nacional de Investigação Agrária e Veterinária, I.P.; Laboratório Ibérico Internacional de Nanotecnologia (LIN); Universidade do Minho; INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência; INEGI - Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial; Instituto Nacional de Saúde Dr. Ricardo Jorge, I.P.; ISPUP - Instituto de Saúde Pública da Universidade do Porto; SPMAQ – Soluções Projectos Maquinas Unipessoal LDA; Fresenius Kabi Pharma Portugal LDA; LABESFAL – Laboratórios Almiro S.A.

Sources of financing:

PRR - Aviso 2021-C05i01-01 PRR

Start date:


(Predicted) End date:


Total budget:

€ 45573,48


Bacterial resistance to antibiotics is currently one of the most relevant public health problems, since many bacteria previously susceptible to commonly used antibiotics no longer respond to these same agents. The development of bacterial resistance to antibiotics is a natural phenomenon resulting from the selective pressure exerted by the use of antibiotics, but which has undergone a very accelerated expansion due to the inappropriate use of these drugs, with a very clear correlation between a higher consumption of antibiotics and higher levels. high levels of microbial resistance.

The SMARTGNOSTICS project, promoted by a consortium of 11 entities (business and ENESII) led by ALS – LIFE SCIENCES PORTUGAL, aims to develop technology, produce and place on the global market solutions capable of detecting and monitoring the existence and microbiotic resistance, in 4 contexts. :

  • Human health
  • Animal health
  • Environment Agrifood Sector

The SMARTgNOSTICS project proposes the development of fast and miniaturized devices to be used in decentralized environments and to allow maximum connectivity. The intended approach, using point-of-care (POC) solutions that can be simultaneously “lab-on-a-chip” (LOC) coupled to the acquisition and processing of data through Artificial Intelligence (AI), will provide data reliable for predictive analysis regarding future AMR genes and/or pathogenic microorganisms and prevention of their propagation in the 4 pillars of the AMR cycle: Human Health, Animal Health, Environment and Food Safety.

Funding: PRR

Research Team