Universitat de Girona
  • Girona, Catalonia, Spain
Recent publications
Background: The assessment of sperm DNA integrity has been proposed as a complementary test to conventional mammalian semen analysis. In this sense, single-strand (SSB) and double-strand (DSB) DNA breaks, the two types of sperm DNA fragmentation (SDF), have been reported to have different aetiologies and to be associated to different fertility outcomes in bovine and humans. Considering that no studies in porcine have addressed how SDF may affect sperm quality and fertility outcomes, the present work aimed to determine the impact of global DNA damage, SSB and DSB on sperm quality and in vitro fertilising ability. To this end, 24 ejaculates (one per boar) were split into three aliquots: the first was used to assess sperm quality parameters through a computer-assisted sperm analysis (CASA) system and flow cytometry; the second was used to perform in vitro fertilisation, and the third, to evaluate sperm DNA integrity using alkaline and neutral Comet assays. Results: The results showed that global DNA damage negatively correlates (P < 0.05) with normal sperm morphology (R = - 0.460) and progressive motility (R = - 0.419), and positively with the percentage of non-viable sperm (R = 0.507). Multiple regression analyses showed that non-viable sperm were related to SSB (β = - 0.754). In addition, while fertilisation did not seem to be affected by sperm DNA integrity, global DNA damage, DSB and SSB were found to be correlated to embryo development outcomes. Specifically, whereas global DNA damage and DSB negatively affected (P < 0.05) the later preimplantation embryo stages (percentage of early blastocyst/blastocyst D6: for global DNA damage, R = - 0.458, and for DSB, R = - 0.551; and percentage of hatching/hatched blastocyst D6: for global DNA damage, R = - 0.505, and for DSB, R = - 0.447), global DNA damage and SSB had a negative impact (P < 0.05) on the developmental competency of fertilised embryos (R = - 0.532 and R = - 0.515, respectively). Remarkably, multiple regression analyses supported the associations found in correlation analyses. Finally, the present work also found that the inclusion of Comet assays to the conventional sperm quality tests improves the prediction of blastocyst formation (AUC = 0.9021, P < 0.05), but not fertilisation rates (P > 0.05). Conclusion: Considering all these findings, this work sets a useful model to study how SDF negatively influences fertility.
To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
In this research we collect, prepare and analyze a geospatial database of maritime activities located in the northern Bay of Bengal with the final aim to simulate maritime spatial planning (MSP) - ready information source for future sectoral and multi-sector MSP in Bangladesh. The database is composed of 28 anthropogenic and environmental layers categorized into seven Blue Economy sectors. The database is analyzed with a set of geospatial models aimed at understanding the intensity distribution of human activities at sea and the potential marine use conflicts emerging from the aggregation of human activities. Ecological resources were characterized in terms of marine mammals, lobsters, commercially important and threatened fish species, and pelagic birds and mapped as biodiversity hotspots using geographic cluster analysis. Results show that the most intensely used sea areas are located along the northeastern coast of Bangladesh, as well as in the Swatch of No Ground (SoNG) area, with maximum Marine Use Intensity (MUI) scores ranging from 5 to 8. Offshore waters of Saint Martin's Island have higher MUI scores (≥ 5) as well. The pairwise spatial conflict analysis shows that nature protection sites particularly SoNG Marine Protected Area (MPA), Nijhum Dwip Marine Reserve (MR), and Saint Martin's Island MPA are exposed to the high Marine Use Conflicts (MUC) induced by fishing and shipping activities. Fishing operations generate the highest MUC value (MUC = 30) in SoNG MPA, whereas shipping activities produce the highest MUC value (MUC = 24) in Nijhum Dwip MR. Both of the MPAs exhibit 6 to 12 MUC scores induced by shipping. The proposed database together with the illustrated analytical techniques used in this study and key findings can provide the first understanding of the priorities for Ecosystem Based Management of Bangladesh's marine space and provide valuable insights on the urgency for MSP process in the country. The study concludes with an outlook on the utility of the database for future analysis.
High-sediment water in drip irrigation (HSWDI) technology offers the opportunity to alleviate water shortages in agricultural irrigation. Emitter clogging, caused by active suspended particles, salt ions, and microorganisms present in water with high sediment load, poses considerable technical challenges to HSWDI. To date, emitter blockage of HSWDI is attributed to physical clogging, little is known about the physical, chemical, and biological clogging behaviors and their interactions for HSWDI. Here, X-ray diffraction and 16S rRNA gene sequencing were applied to determine the physicochemical minerals and microbial community structure of the foulants for HSWDI, using three types of flat emitters and two fertilization modes (no-fertigation and fertigation with ammonium polyphosphate, APP). Results indicated that HSWDI emitter clogging was not only caused by physical clogging (induced by particulates) but also caused by chemical clogging (i.e., precipitates) and biological clogging (i.e., biofilms). The main particulates in HSWDI were found to be quartz (accounting for 41.8–56.3% of total clogging foulants) and feldspar (13.6–21.1%), while the precipitates that contained calcite, dolomite and aragonite contributed 14.6–26.7%. The dominant flora in foulants were Proteobacteria (relative abundance ranged: 41.7–53.9%) and Bacteroidetes (13.6–17.3%). Moreover, the coupling effect of three types of fouling was the main reason affecting clogging (accounting for 36.3%), while the effect of two or single fouling was less (accounting for 14.4–25.3% and 0.7–2.6%). In addition, APP application caused the increase in microbial diversity and the proliferation of microorganisms, resulting in the interactions between biofilm and other two foulants (i.e., precipitates and particulates) were exacerbated, thus aggravating emitter clogging. This study opens a frontier for the investigation of physical, chemical, and biological clogging behavior, in-depth clogging mechanisms, and anti-clogging measures for HSWDI, which will facilitate the utilization of high-sediment water in agricultural irrigation.
What are the things that we think matter morally, and how do societal factors influence this? To date, research has explored several individual-level and historical factors that influence the size of our ‘moral circles.' There has, however, been less attention focused on which societal factors play a role. We present the first multi-national exploration of moral expansiveness—that is, the size of people’s moral circles across countries. We found low generalized trust, greater perceptions of a breakdown in the social fabric of society, and greater perceived economic inequality were associated with smaller moral circles. Generalized trust also helped explain the effects of perceived inequality on lower levels of moral inclusiveness. Other inequality indicators (i.e., Gini coefficients) were, however, unrelated to moral expansiveness. These findings suggest that societal factors, especially those associated with generalized trust, may influence the size of our moral circles.
Research has consistently found that gratitude predicts life satisfaction. Unfortunately, only a few underlying psychological processes (e.g., mediators) have been tested, using only cross-sectional designs. Nonetheless, novel methodological research argues that mediations should be tested using only longitudinal or experimental data. Thus, we extended current research into the gratitude-life satisfaction link by testing, longitudinally, two unexplored mediators: the satisfaction (BPNS) and frustration (BNPF) of the three basic psychological needs for autonomy, competence and relatedness as proposed by Self-determination theory (SDT). A three-wave longitudinal design among a representative sample of Chilean adults found support for our hypothesis: Gratitude at T1 predicts higher BPNS and lower BPNF at T2, which in turns predicts higher life satisfaction at T3. Key theoretical and practical implications for gratitude and SDT research are discussed.
Hydrogenation of polyalphaolefins (PAO) is an industrial process catalyzed by supported precious metals. In this regard, halloysite clay (Hal) has been proven as an efficient support for the immobilization of Pd nanoparticles and development of high performance catalysts under mild reaction condition. In this research, the effect of Hal hydrophobicity on the PAO hydrofinishing efficiency is studied. In this line, cetrimonium bromide (CTAB) was used for adjusting the hydrophobicity of halloysite surface. Three catalysts, Hal/Pd, Hal/Pd/CTAB and Hal/CTAB/Pd, were fabricated by palladation of Hal, treating palladated Hal with CTAB and palladation of CTAB‐treated Hal, respectively. The catalysts were characterized and their activity for the hydrogenation of PAO was appraised. Moreover, a molecular simulation approach was employed to survey the effect of surface hydrophobicity of Hal on the alkene hydrogenation energy diagram and the steric maps of the main catalytic stages. Both experimental and computational studies approved that the presence of CTAB detracts the activity of the catalyst. Moreover, the order of introduction of Pd and CTAB affects the content of incorporated CTAB and Pd and Pd particle size and the order of catalysts activity was as follows: Hal/Pd > Hal/Pd/CTAB > Hal/CTAB/Pd. In fact, 5 wt. % Hal/Pd promoted the hydrogenation at 130 °C and hydrogen pressure of 8 bar to furnish 98% hydrogenated PAO.
Global change is modifying meteorological and hydrological factors that influence the thermal regime of water bodies. These modifications can lead to longer stratification periods with enlarged hypolimnetic anoxic periods, which can promote heterotrophic anaerobic processes and alter reservoir carbon cycling. Here, we quantified aerobic and anaerobic heterotrophic processes (aerobic respiration, denitrification, iron and manganese reduction, sulfate reduction, and methanogenesis) on dissolved inorganic carbon (DIC) production in the hypolimnion of a Mediterranean reservoir (El Gergal, Spain) under two contrasting hydrological conditions: a wet year with heavy direct rainfall and frequent water inputs from upstream reservoirs, and a dry year with scarce rainfall and negligible water inputs. During the wet year, water inputs and rainfall induced low water column thermal stability and earlier turnover. By contrast, thermal stratification was longer and more stable during the dry year. During wet conditions, we observed lower DIC accumulation in the hypolimnion, mainly due to weaker sulfate reduction and methanogenesis. By contrast, longer stratification during the dry year promoted higher hypolimnetic DIC accumulation, resulting from enhanced methanogenesis and sulfate reduction, thus increasing methane emissions and impairing reservoir water quality. Aerobic respiration, denitrification and metal reduction produced a similar amount of DIC in the hypolimnion during the two studied years. All in all, biological and geochemical (calcite dissolution) processes explained most of hypolimnetic DIC accumulation during stratification regardless of the hydrological conditions, but there is still ~ 30% of hypolimnetic DIC production that cannot be explained by the processes contemplated in this study and the assumptions made.
In this work a wide sample analysis, under similar conditions, has been carried out and a calibration strategy based on a careful selection of input variables combined with sensitivity analysis has enabled us to build accurate neural network models, with high correlation (R > 0.99), for the prediction of the aspect ratio of micro/nanofiber products. The model is based on cellulose content, applied energy, fiber length and diameter of the pre-treated pulps. The number of samples used to generate the neural network model was relatively low, consisting of just 15 samples coming from pine pulps that had undergone thermomechanical, kraft and bleached kraft treatments to produce a significant range of aspect ratio. However, the ANN model, involving 4 inputs and 4 hidden neurons and calibrated on the basis of pine dataset, was accurate and robust enough to predict the aspect ratio of micro/nanofiber materials obtained from other cellulose sources including very different softwood and hardwood species such as Spruce, Eucalyptus and Aspen (R = 0.84). The neural network model was able to capture the nonlinearities involved in the data providing insight about the profile of the aspect ratio achieved with further homogenization during the fibrillation process.
expenditure to overcome drag and hold a stationary position. Sexual dimorphism in morphological traits did not lead to sexual differences in oxygen uptake. Moreover, we found that individuals operated close to their maximum aerobic capacity at elevated current velocities (≥ 25 cm s −1). This suggested that the high flow-driven energetic demand may compromise the energy available for reproduction, growth and dispersal , thereby affecting overall fitness. These metabolic constraints could partly explain the failed invasions of invasive crayfish in fast-flowing waters.
Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.
Con base en el Enfoque de los Itinerarios de Enseñanza de las Matemáticas (EIEM), que propone secuencias de enseñanza intencionadas desde lo concreto hasta lo simbólico, se analiza cómo influye el contexto de enseñanza en las tareas con patrones de repetición en un grupo de 24 escolares españoles durante dos cursos académicos consecutivos (4-6 años). Para ello, se han implementado tareas de patrones de repetición de los dos contextos extremos de un itinerario previamente diseñado y validado: situaciones reales y contextos gráficos, respectivamente. Los datos se han analizado a partir de esquemas metodológicos etnográficos de observación participante (diario de campo); la documentación pedagógica (registro audiovisual); y las producciones escritas de los patrones (representaciones). Los principales resultados obtenidos muestran que: a) en el alumnado de 4-5 años se ha identificado una diferencia positiva del 32,9 % de las situaciones reales frente a los recursos gráficos; b) en el alumnado de 5-6 años, si bien desciende ligeramente dicha diferencia entre ambos contextos, continúa estando por encima del 30 %. Se concluye que el contexto de enseñanza influye en la comprensión de los patrones de repetición, por lo que es necesaria una enseñanza de los patrones desde el nivel situacional hasta el formal.
Background Previous studies have suggested a relationship between human papillomavirus vaccine and autoimmune diseases, including thyroiditis. Thus, we aimed to evaluate the risk of thyroiditis associated with HPV vaccination among girls using the Primary Care Database For Pharmacoepidemiological Research (BIFAP) in Spain. Methods In this retrospective cohort study, girls in BIFAP aged 9–18 years from 2007 to 2016, free of past thyroiditis and HPV vaccination, were included. Hazard Ratios (HRs; 95% CI) of thyroiditis were calculated within exposed periods (up to 2 years of vaccination) and post-exposed periods (from 2 years after vaccination onwards) compared with non-exposed periods, overall, by dose and by type of vaccine, adjusted for potential confounders collected at different times. In a post-hoc analysis, we moved back the thyroiditis date (30 days) as a theoretical delay in diagnosis. Results Out of the 388,411 girls included in the cohort, 153,924 were vaccinated against HPV and 480 thyroiditis (253 autoimmune) cases were identified (334 non-exposed; 103 exposed; 43 post-exposed). Adjusted HR was 1.18 [95% CI: 0.79–1.76] for exposed (1.25 [0.77–2.04] for bi- and 1.15 [0.76–1.76] for quadri-valent vaccines) and 1.26 [0.74–2.14] for post-exposed periods. HR was 1.50 [0.87–2.59] for the 1st dose, 1.13 [0.66–1.91] for the 2nd and 1.11 [0.71–1.72] for the 3rd one. When the diagnosis date was moved back, the risk was 1.14 [0.76–1.70] for exposed period, being 1.80 [0.86–3.76] and 1.40 [0.74–2.66] after 1st dose of bi- and quadri-valent, respectively. Conclusions We did not observe an increased risk of thyroiditis following HPV vaccination (whether bi- or quadri-valent). Even though the point estimate was higher after 1st HPV vaccination dose than after subsequent doses, a dose–effect was not confirmed. Results remained similar after applying a lag time.
This paper examines the subjective well-being (SWB) of children and adolescents (10‒18 years old) during the COVID-19 pandemic in Indonesia for two periods (May to July 2020 and March to May 2021), using cross-sectional data from two distinct samples of N = 1,011 (M age = 14.61) and N = 1,640 (M age = 14.86), respectively. Its aims are twofold: (1) to examine the state of SWB among Indonesian children, including its cognitive component (measured using the CW-SWBS), positive affect (PA), and negative affect (NA), and the participants’ satisfaction with their contact with friends and how they spend their time; and (2) to compare the evolution of these SWB-related aspects from the first to the second year of the COVID-19 pandemic. Data were collected using Google Forms and convenience and snowball sampling. Results showed that boys displayed significantly higher mean SWB scores than girls, while elementary students displayed significantly higher mean scores for the cognitive component than middle and high school students for both data collection periods. Boys also displayed significantly higher mean PA scores than girls. There were significant school grade differences on PA and NA, depending on the period of study. In the first year of the COVID-19 pandemic, children and adolescents displayed lower scores on satisfaction with their contact with friends than in the second year of the COVID-19 pandemic. These results suggest that children and adolescents adapted to the COVID-19 situation during the second year, and this adaptation protected their SWB from further decrease, as defended by the homeostasis theory.
In this study, Fe 75 Si 12 Ti 6 B 7 and Fe 73 Si 15 Ti 5 B 7 (wt. %) alloy powders were synthesized from pure elemental powders by a mechanical alloying (MA) method under argon atmosphere. The evolution in particles morphology, chemical composition, crystalline structure, magnetic and hyperfine proprieties of the mixture elements during MA(0-80 h) was investigated by scanning electron microscopy attached with energy-dispersive spectroscopy, X-ray diffraction (XRD), vibrating sample magnetometer and Mö ssbauer spectroscopy (MS). The Rietveld refinement of the XRD pattern of the samples milled 5 h shows the formation of several structures: Fe 3 Si, a-Fe nanostructured, Fe 2 Ti and Fe 2 B, in addition, that the structure became much more amorphous together with increasing milling time at 80 h. The thermal stability of powders milled was characterized by differential scanning calorimetry (DSC). The annealing of samples milled 80 h shows that the crystallization of the amorphous phases and the activation energy determined by using Kissinger's equation was 462.23 ± 16.11 kJ mol-1 and 798.43 ± 16.12 kJ mol-1 for the Fe 73 Si 15 Ti 5 B 7 and Fe 75 Si 12 Ti 6 B 7 , respectively. Moreover, the results from XRD and DSC for 80 h of milling were approved by the Mössbauer spectroscopy, and the spectra revealed that the sample Fe 73 Si 15 Ti 5 B 7 is fully amorphous, but the sample Fe 75 Si 12 Ti 6 B 7 still contains some of Fe with a 2.8% fraction non-detected by XRD. The saturation magnetization (Ms) and coercivity values were of about 151 emu/g, 38.5.
Objective and Background: This research aims to develop a theoretical service quality (SQ) model for direct-to-consumer (DTC) telemedicine consultations. Although it can change care delivery for the better, it is crucial to create the appropriate measurement tool to collect and analyze patient's perceptions of SQ to identify any service pitfall and encourage a faster adoption. To the best of the authors' knowledge, this article is one of the first to investigate and propose a SQ model for DTC telemedicine consultations. This study is therefore motivated by a clear need for such a model as it is currently inexistent. Methods: A literature review of health and e-service quality (e-SQ) models was conducted to identify a suitable instrument for the research. A total of 60 studies were included. Results: The main findings are threefold: (1) DTC telemedicine SQ is interdisciplinary: it encompasses generic and context-specific dimensions from the health, e-SQ, and information system literature; (2) the existing SQ models are not adequate, they do not cover all dimensions of DTC telemedicine services; (3) although LeRouge et al.'s Telemedicine service encounter quality model was identified as a reference model, it is inadequate to simply transpose it to the context of the study. Thus, the elaboration of a more suitable instrument and creation of a new updated model by the authors. Conclusion: The conceptual model captures three primary dimensions (system quality, interaction quality and use quality) that represent SQ of DTC telemedicine consultations from a patient perspective.
Background Knowledge about human exposure and health effects associated with non-routinely monitored disinfection by-products (DBPs) in drinking water is sparse. Objective To provide insights to estimate exposure to regulated and non-regulated DBPs in drinking water. Methods We collected tap water from homes (N = 42), bottled water (N = 10), filtered tap water with domestic activated carbon jars (N = 6) and reverse osmosis (N = 5), and urine (N = 39) samples of participants from Barcelona, Spain. We analyzed 11 haloacetic acids (HAAs), 4 trihalomethanes (THMs), 4 haloacetonitriles (HANs), 2 haloketones, chlorate, chlorite, and trichloronitromethane in water and HAAs in urine samples. Personal information on water intake and socio-demographics was ascertained in the study population (N = 39) through questionnaires. Statistical models were developed based on THMs as explanatory variables using multivariate linear regression and machine learning techniques to predict non-regulated DBPs. Results Chlorate, THMs, HAAs, and HANs were quantified in 98–100% tap water samples with median concentration of 214, 42, 18, and 3.2 μg/L, respectively. Multivariate linear regression models had similar or higher goodness of fit (R2) compared to machine learning models. Multivariate linear models for dichloro-, trichloro-, and bromodichloroacetic acid, dichloroacetonitrile, bromochloroacetonitrile, dibromoacetonitrile, trichloropropnanone, and chlorite showed good predictive ability (R² = 0.8–0.9) as 80–90% of total variance could be explained by THM concentrations. Activated carbon filters reduced DBP concentrations to a variable extent (27–80%), and reverse osmosis reduced DBP concentrations ≥98%. Only chlorate was detected in bottled water samples (N = 3), with median = 13.0 µg/L. Creatinine-adjusted trichloroacetic acid was the most frequently detected HAA in urine samples (69.2%), and moderately correlated with estimated drinking water intake (r = 0.48). Significance Findings provide valuable insights for DBP exposure assessment in epidemiological studies. Validation of predictive models in a larger number of samples and replication in different settings is warranted. Impact statement Our study focused on assessing and describing the occurrence of several classes of DBPs in drinking water and developing exposure models of good predictive ability for non-regulated DBPs.
The development of new biomarkers for human male infertility is crucial to improve the diagnosis and the prognosis of this disease. Recently, seminal microbiota was shown to be related to sperm quality parameters, suggesting an effect in human fertility and postulating it as a biomarker candidate. However, its relationship to sperm DNA integrity has not been studied yet. The aim of the present study is to characterize the seminal microbiota of a western Mediterranean population and to evaluate its relationship to sperm chromatin integrity parameters, and oxidative stress. For that purpose, 14 samples from sperm donors and 42 samples from infertile idiopathic patients were obtained and were analyzed to assess the composition of the microbiota through full-length 16S rRNA gene sequencing (Illumina MiSeq platform). Microbial diversity and relative abundances were compared to classic sperm quality parameters (macroscopic semen parameters, motility, morphology and concentration), chromatin integrity (global DNA damage, double-stranded DNA breaks and DNA protamination status) and oxidative stress levels (oxidation-reduction potential). The seminal microbiota observed of these samples belonged to the phyla Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes. The most abundant genera were Finegoldia, Peptoniphilus, Anaerococcus, Campylobacter, Streptococcus, Staphylococcus, Moraxella, Prevotella, Ezakiella, Corynebacterium and Lactobacillus. To our knowledge, this is the first detection of Ezakiella genus in seminal samples. Two clusters of microbial profiles were built based on a clustering analysis, and specific genera were found with different frequencies in relation to seminal quality defects. The abundances of several bacteria negatively correlate with the sperm global DNA fragmentation, most notably Moraxella, Brevundimonas and Flavobacterium. The latter two were also associated with higher sperm motility and Brevundimonas additionally with lower oxidative-reduction potential. Actinomycetaceae, Ralstonia and Paenibacillus correlated with reduced chromatin protamination status and increased double-stranded DNA fragmentation. These effects on DNA integrity coincide in many cases with the metabolism or enzymatic activities of these genera. Significant differences between fertile and infertile men were found in the relative presence of the Propionibacteriaceae family and the Cutibacterium, Rhodopseudomonas and Oligotropha genera, which supports its possible involvement in male fertility. Our findings sustain the hypothesis that the seminal microbiome has an effect on male fertility.
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3,080 members
Lluis Coromina
  • Department of Economics
Albert Ruda
  • Departament de dret privat
Miquel Duran
  • Institute of Computational Chemistry
Sadurní Martí
  • Department of Philology and Communication
Angels Xabadia
  • Department of Economics
Pl. Sant Domènec, 3, 17004, Girona, Catalonia, Spain
Head of institution
Dr. Quim Salvi