Raw milk cheeses from Beira Baixa, Portugal-A contributive study for the microbiological hygiene and safety assessment.

Due to specific bacterial microbiota, raw milk cheeses have appreciated sensory properties. However, they may pose a threat to consumer safety due to potential pathogens presence. This study evaluated the microbiological contamination of 98 raw milk cheeses from Beira Baixa, Portugal. Presence and enumeration of Coagulase Positive Staphylococci (CPS), Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, and indicator microorganisms (non-pathogenic E. coli and Listeria spp.) was attained. E. coli antimicrobial resistance (AMR) was also evaluated. PCR and/or Whole genome sequencing (WGS) was used to characterize E. coli, Salmonella spp. and L. monocytogenes isolates. Sixteen cheeses (16.3%) were classified as Satisfactory, 59 (60.2%) as Borderline and 23 (23.5%) as Unsatisfactory/Potential Injurious to Health. L. monocytogenes, CPS > 104 cfu g-1, Extraintestinal pathogenic E. coli (ExPEC) and Salmonella spp. were detected in 4.1%, 6.1%, 3.1% and 1.0% of the samples, respectively. Listeria innocua (4.1%) and E. coli > 104 cfu g-1 (16.3%) were also detected. AMR E. coli was detected in 23/98 (23.5%) of the cheese samples, of which two were multidrug resistant. WGS identified genotypes already associated to human disease and Listeria spp. cluster analysis indicated that cheese contamination might be related with noncompliance with Good Hygiene Practices during cheese production.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Orogenital and anal infection by Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, and other sexually transmitted infections in men who have sex with men in Lisbon.

BACKGROUND: Men who have sex with men (MSM) are at risk for sexually transmitted infections (STIs), but more data on extragenital carriage are needed. AIM: We assessed the genital and extragenital prevalence of bacterial and other STIs in MSM in a Lisbon sexual health clinic. METHODS: We screened oral, anal, and urine samples of MSM visiting the GAT-CheckpointLX clinic June 2017-December 2021 for Chlamydia trachomatis (including lymphogranuloma venereum, LGV), Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis, Mycoplasma hominis, Ureaplasma urealyticum, and U. parvum. Ano-oro-genital lesions were tested for LGV, Treponema pallidum, and Herpes Simplex Virus. Blood was tested for HIV and T. pallidum antibodies. RESULTS: N. gonorrhoeae was found in 16.6% of the MSM followed by C. trachomatis (13.2%), M. genitalium (10.3%) and T. vaginalis (0.2%). The most frequent occurrence was anorectal (C. trachomatis, M. genitalium) and oral (N. gonorrhoeae). We found high carriage of U. urealyticum (36.1%) and M. hominis (22.1%). LGV was detected in 21.8% of chlamydia-positive anorectal swabs. Syphilis was detected in 22.6% of tested MSM, while 13.8% had HIV. Gonorrhoea and chlamydia were significantly more prevalent in MSM with concomitant HIV or syphilis. CONCLUSION: The substantial extragenital prevalence of bacterial STIs in MSM, and HIV and syphilis coinfections, suggest screening has value in identifying hidden carriage and in contributing for providing better care.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

Vertical Transmission of SARS-CoV-2-Specific Antibodies and Cytokine Profiles in Pregnancy.

Despite intensive characterization of immune responses after COVID-19 infection and vaccination, research examining protective correlates of vertical transmission in pregnancy are limited. Herein, we profiled humoral and cellular characteristics in pregnant women infected or vaccinated at different trimesters and in their corresponding newborns. We noted a significant correlation between spike S1-specific IgG antibody and its RBD-ACE2 blocking activity (receptor-binding domain-human angiotensin-converting enzyme 2) in maternal and cord plasma (P < .001, R > 0.90). Blocking activity of spike S1-specific IgG was significantly higher in pregnant women infected during the third trimester than the first and second trimesters. Elevated levels of 28 cytokines/chemokines, mainly proinflammatory, were noted in maternal plasma with infection at delivery, while cord plasma with maternal infection 2 weeks before delivery exhibited the emergence of anti-inflammatory cytokines. Our data support vertical transmission of protective SARS-CoV-2-specific antibodies. This vertical antibody transmission and the presence of anti-inflammatory cytokines in cord blood may offset adverse outcomes of inflammation in exposed newborns.

Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer.

Endoxifen, a secondary tamoxifen metabolite, is a potent antiestrogen exhibiting estrogen receptor alpha (ERα) binding at nanomolar concentrations. Phase I/II clinical trials identified clinical activity of Z-endoxifen (ENDX), in endocrine-refractory metastatic breast cancer as well as ERα+ solid tumors, raising the possibility that ENDX may have a second, ERα-independent, mechanism of action. An unbiased mass spectrometry approach revealed that ENDX concentrations achieved clinically with direct ENDX administration (5 µM), but not low concentrations observed during tamoxifen treatment (<0.1 µM), profoundly altered the phosphoproteome of the aromatase expressing MCF7AC1 cells with limited impact on the total proteome. Computational analysis revealed protein kinase C beta (PKCß) and protein kinase B alpha or AKT1 as potential kinases responsible for mediating ENDX effects on protein phosphorylation. ENDX more potently inhibited PKCß1 kinase activity compared to other PKC isoforms, and ENDX binding to PKCß1 was confirmed using Surface Plasma Resonance. Under conditions that activated PKC/AKT signaling, ENDX induced PKCß1 degradation, attenuated PKCß1-activated AKTSer473 phosphorylation, diminished AKT substrate phosphorylation, and induced apoptosis. ENDX's effects on AKT were phenocopied by siRNA-mediated PKCß1 knockdown or treatment with the pan-AKT inhibitor, MK-2206, while overexpression of constitutively active AKT diminished ENDX-induced apoptosis. These findings, which identify PKCß1 as an ENDX target, indicate that PKCß1/ENDX interactions suppress AKT signaling and induce apoptosis in breast cancer.

Prevention, protocols, and lab capacity: lessons from a norovirus outbreak in the Algarve.

This brief report presents the findings of an epidemiological investigation into a large-scale outbreak of norovirus gastroenteritis that occurred in a hotel in Algarve, Portugal, in August 2022. A total of 244 cases were reported, primarily affecting Portuguese families, with the parents aged 40-50 years and the children aged 0-19 years. Reported symptoms included vomiting, nausea, abdominal pain, and diarrhoea. Norovirus genotype GI.3 [P3] was detected in stool samples from eight probable cases, while food samples tested negative for norovirus and common pathogenic bacteria. The investigation data collected suggest that the source of the outbreak was likely in the hotel's common areas, with subsequent person-to-person transmission in other areas. The final report emphasizes the importance of improving outbreak prevention and control measures, including the development of a foodborne outbreak investigation protocol, the establishment of an outbreak response team, and the enhancement of regional laboratory capacity.

LDPE and PLA Active Food Packaging Incorporated with Lemon by-Products Extract: Preparation, Characterization and Effectiveness to Delay Lipid Oxidation in Almonds and Beef Meat.

Low-density polyethylene-based packaging with 4% lemon extract (LDPE/4LE) and two polylactic-based (PLA) packaging materials with 4% and 6% lemon extract (PLA/PEG/4LE and PLA/6LE) were produced. O2 and water permeability tests were performed, the total and individual phenolic compounds content were measured, and the films' antioxidant activities were determined. The films' ability to delay lipid oxidation was tested in two model foods: almonds, packaged with LDPE/4LE, PLA/4LE and PLA/6LE for a maximum period of 60 days at 40 °C (accelerated assay); and beef meat, packaged with the PLA/6LE for a maximum period of 11 days at 4 °C. The LE improved the WVP in all of the active films by 33%, 20% and 60% for the LDPE/4LE, PLA/4LE and PLA/6LE films, respectively. At the end of 10 days, the migration of phenolic compounds through the PLA films was measured to be 142.27 and 114.9 µg/dm2 for the PLA/4LE and PLA/6LE films, respectively, and was significantly higher than phenolic compounds migration measured for the LDPE/4LE (15.97 µg/dm2). Naringenin, apigenin, ferulic acid, eriocitrin, hesperidin and 4-hydroxybenzoic acid were the main identified compounds in the PLA, but only 4-hydroxybenzoic acid, naringenin and p-coumaric acid were identified in the LDPE films. Regarding the films' ability to delay lipid oxidation, LDPE/4LE presented the best results, showing a capacity to delay lipid oxidation in almonds for 30 days. When applied to raw beef meat, the PLA/6LE packaging was able to significantly inhibit lipid oxidation for 6 days, and successfully inhibited total microorganisms' growth until the 8th day of storage.

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Relationship between BCL2 mutations and follicular lymphoma outcome in the chemoimmunotherapy era.

How to identify follicular lymphoma (FL) patients with low disease burden but high risk for early progression is unclear. Building on a prior study demonstrating the early transformation of FLs with high variant allele frequency (VAF) BCL2 mutations at activation-induced cytidine deaminase (AICDA) sites, we examined 11 AICDA mutational targets, including BCL2, BCL6, PAX5, PIM1, RHOH, SOCS, and MYC, in 199 newly diagnosed grade 1 and 2 FLs. BCL2 mutations with VAF ≥20% occurred in 52% of cases. Among 97 FL patients who did not initially receive rituximab-containing therapy, nonsynonymous BCL2 mutations at VAF ≥20% were associated with increased transformation risk (HR 3.01, 95% CI 1.04-8.78, p = 0.043) and a trend toward shorter event-free survival (EFS, median 20 months with mutations versus 54 months without, p = 0.052). Other sequenced genes were less frequently mutated and did not increase the prognostic value of the panel. Across the entire population, nonsynonymous BCL2 mutations at VAF ≥20% were associated with decreased EFS (HR 1.55, 95% CI 1.02-2.35, p = 0.043 after correction for FLIPI and treatment) and decreased overall survival after median 14-year follow-up (HR 1.82, 95% CI 1.05-3.17, p = 0.034). Thus, high VAF nonsynonymous BCL2 mutations remain prognostic even in the chemoimmunotherapy era.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms.

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Manifold epigenetics: A conceptual model that guides engineering strategies to improve whole-body regenerative health.

Despite the promising advances in regenerative medicine, there is a critical need for improved therapies. For example, delaying aging and improving healthspan is an imminent societal challenge. Our ability to identify biological cues as well as communications between cells and organs are keys to enhance regenerative health and improve patient care. Epigenetics represents one of the major biological mechanisms involving in tissue regeneration, and therefore can be viewed as a systemic (body-wide) control. However, how epigenetic regulations concertedly lead to the development of biological memories at the whole-body level remains unclear. Here, we review the evolving definitions of epigenetics and identify missing links. We then propose our Manifold Epigenetic Model (MEMo) as a conceptual framework to explain how epigenetic memory arises and discuss what strategies can be applied to manipulate the body-wide memory. In summary we provide a conceptual roadmap for the development of new engineering approaches to improve regenerative health.

Listeria monocytogenes, Escherichia coli and Coagulase Positive Staphylococci in Cured Raw Milk Cheese from Alentejo Region, Portugal.

Traditional cheeses are part of the Portuguese gastronomic identity, and raw milk of autochthonous species is a common primary ingredient. Here, we investigated the presence of Listeria monocytogenes, Coagulase Positive Staphylococci (CPS) and pathogenic Escherichia coli, as well as of indicator microorganisms (E. coli and other Listeria spp.) in 96 cured raw milk cheeses from the Alentejo region. Whole genome sequencing (WGS) of pathogenic E. coli and Listeria spp. as well as antimicrobial resistance (AMR) screening of E. coli isolates was also performed. L. monocytogenes, CPS > 104 cfu/g and Extraintestinal E. coli were detected in 15.6%, 16.9% and 10.1% of the samples, respectively. Moreover, L. monocytogenes > 102 cfu/g and Staphylococcal enterotoxins were detected in 4.2% and 2.2% of the samples, respectively. AMR was observed in 27.3% of the E. coli isolates, six of which were multidrug resistant. WGS analysis unveiled clusters of high closely related isolates for both L. monocytogenes and L. innocua (often correlating with the cheese producer). This study can indicate poor hygiene practices during milk collection/preservation or during cheese-making procedures and handling, and highlights the need of more effective prevention and control measures and of multi-sectoral WGS data integration, in order to prevent and detect foodborne bacterial outbreaks.

A BAK subdomain that binds mitochondrial lipids selectively and releases cytochrome C.

How BAK and BAX induce mitochondrial outer membrane (MOM) permeabilization (MOMP) during apoptosis is incompletely understood. Here we have used molecular dynamics simulations, surface plasmon resonance, and assays for membrane permeabilization in vitro and in vivo to assess the structure and function of selected BAK subdomains and their derivatives. Results of these studies demonstrate that BAK helical regions α5 and α6 bind the MOM lipid cardiolipin. While individual peptides corresponding to these helical regions lack the full biological activity of BAK, tandem peptides corresponding to α4-α5, α5-α6, or α6-α7/8 can localize exogenous proteins to mitochondria, permeabilize liposomes composed of MOM lipids, and cause MOMP in the absence of the remainder of the BAK protein. Importantly, the ability of these tandem helices to induce MOMP under cell-free conditions is diminished by mutations that disrupt the U-shaped helix-turn-helix structure of the tandem peptides or decrease their lipid binding. Likewise, BAK-induced apoptosis in intact cells is diminished by CLS1 gene interruption, which decreases mitochondrial cardiolipin content, or by BAK mutations that disrupt the U-shaped tandem peptide structure or diminish lipid binding. Collectively, these results suggest that BAK structural rearrangements during apoptosis might mobilize helices involved in specific protein-lipid interactions that are critical for MOMP.

Pathogenic Escherichia coli, Salmonella spp. and Campylobacter spp. in Two Natural Conservation Centers of Wildlife in Portugal: Genotypic and Phenotypic Characterization.

Human-wildlife coexistence may increase the potential risk of direct transmission of emergent or re-emergent zoonotic pathogens to humans. Intending to assess the occurrence of three important foodborne pathogens in wild animals of two wildlife conservation centers in Portugal, we investigated 132 fecal samples for the presence of Escherichia coli (Shiga toxin-producing E. coli (STEC) and non-STEC), Salmonella spp. and Campylobacter spp. A genotypic search for genes having virulence and antimicrobial resistance (AMR) was performed by means of PCR and Whole-Genome Sequencing (WGS) and phenotypic (serotyping and AMR profiles) characterization. Overall, 62 samples tested positive for at least one of these species: 27.3% for STEC, 11.4% for non-STEC, 3.0% for Salmonella spp. and 6.8% for Campylobacter spp. AMR was detected in four E. coli isolates and the only Campylobacter coli isolated in this study. WGS analysis revealed that 57.7% (30/52) of pathogenic E. coli integrated genetic clusters of highly closely related isolates (often involving different animal species), supporting the circulation and transmission of different pathogenic E. coli strains in the studied areas. These results support the idea that the health of humans, animals and ecosystems are interconnected, reinforcing the importance of a One Health approach to better monitor and control public health threats.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma.

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Although only a few recurrent somatic mutations have been identified, chromosomal abnormalities, including the loss of heterozygosity (LOH) at the chromosome 1p and gains of chromosome 17q, are often seen in the high-risk cases. The biological basis and evolutionary forces that drive such genetic abnormalities remain enigmatic. Here, we conceptualize the Gene Utility Model (GUM) that seeks to identify genes driving biological signaling via their collective gene utilities and apply it to understand the impact of those differentially utilized genes on constraining the evolution of NB karyotypes. By employing a computational process-guided flow algorithm to model gene utility in protein-protein networks that built based on transcriptomic data, we conducted several pairwise comparative analyses to uncover genes with differential utilities in stage 4 NBs with distinct classification. We then constructed a utility karyotype by mapping these differentially utilized genes to their respective chromosomal loci. Intriguingly, hotspots of the utility karyotype, to certain extent, can consistently recapitulate the major chromosomal abnormalities of NBs and also provides clues to yet identified predisposition sites. Hence, our study not only provides a new look, from a gene utility perspective, into the known chromosomal abnormalities detected by integrative genomic sequencing efforts, but also offers new insights into the etiology of NB and provides a framework to facilitate the identification of novel therapeutic targets for this devastating childhood cancer.

Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View.

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.

Corynebacterium striatum cardiac device-related endocarditis: A case report.

Corynebacterium striatum is an emerging Gram-positive bacillus associated with invasive infection in both immunocompetent and immunocompromised patients, especially associated with medical devices. Its ability to form biofilms has been demonstrated and it has been occasionally associated with cardiac device-related infective endocarditis with few cases described in literature. We report a case of C. striatum cardiac device-related infective endocarditis of complex management.

Genotypic and Phenotypic Characterization of Pathogenic Escherichia coli, Salmonella spp., and Campylobacter spp., in Free-Living Birds in Mainland Portugal.

Birds are potential carriers of pathogens affecting humans and agriculture. Aiming to evaluate the occurrence of the top three most important foodborne pathogens in free-living birds in Portugal, we investigated 108 individual fecal samples from free-living birds and one pooled sample of gull feces (n = 50) for the presence of Escherichia coli (pathogenic and non-pathogenic), Salmonella spp. and Campylobacter spp. Virulence- and antimicrobial resistance- (AMR) associated genes were detected by PCR and Whole-Genome Sequencing (WGS), and phenotypic (serotyping and AMR profiles) characterization was performed. Overall, 8.9% of samples tested positive for pathogenic E. coli, 2.8% for Salmonella spp., and 9.9% for Campylobacter spp. AMR was performed on all pathogenic isolates and in a fraction of non-pathogenic E. coli, being detected in 25.9% of them. Ten of the tested E. coli isolates were multidrug-resistant (MDR), and seven of them were Extended-spectrum ß-lactamase (ESBL) producers. Among Salmonella (n = 3) and Campylobacter (n = 9), only one strain of C. jejuni was identified as MDR. Most of the identified serotypes/sequence types had already been found to be associated with human disease. These results show that free-living birds in Portugal may act as carriers of foodborne pathogens linked to human disease, some of them resistant to critically important antimicrobials.