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Carrots, rich in carotenoids and other bioactive compounds, are a promising raw material for value-added applications such as nutricosmetics. Nutricosmetics represent a rapidly expanding segment of the beauty and wellness industry, driven by rising consumer interest in natural ingredients and health-focused products. However, the use of carrots as bio-ingredients in nutricosmetics remains limited due to a disconnect between production systems, scientific research, and market expectations. This study integrates bibliometric, social media, consumer-survey, market-trend, and foreign-trade analyses to identify the key gaps hindering the valorization of carrots within this industry. A systematic literature analysis showed a strong emphasis on postharvest quality and bioactive characterization (approximately 70% of the dominant thematic focus), with minimal attention to commercialization or circular-economy frameworks (less than 5%). Social media results revealed that public discourse is dominated by culinary and gardening themes (around 82% of extracted mentions), with very limited awareness of cosmetic or wellness applications (below 10%). Google Trends demonstrated moderate global growth in interest in nutricosmetics (approximately 28% increase over the analyzed period), with higher activity concentrated in Spanish-speaking countries (about 63% of the top interest locations). Consumer surveys in Colombia (n = 191) indicated that 70.16% of respondents were unfamiliar with the term “nutricosmetics,” though 54.45% reported consuming such products once definitions were provided, revealing a latent market potential. Trade analysis highlighted Colombia’s dependence on high-value imported ingredients, despite existing capacity to export value-added goods. Together, these findings reveal structural gaps between research, industry, and consumer awareness, offering a roadmap for positioning carrots as a viable ingredient for value-added applications in the nutricosmetics sector.
Background Respiratory Syncytial Virus (RSV) is one of the leading causes of acute lower respiratory tract infections in Colombia, therefore, as no widespread antiviral exists, maternal immunization using RSV prefusion F protein vaccine (RSVpreF) represents a promising strategy. This study aims to evaluate the cost-utility of the RSVpreF maternal vaccine in preventing RSV infections among Colombian infants. Methods We used a Markov model to simulate a Colombian birth cohort over a lifetime horizon, comparing maternal RSVpreF vaccination with no intervention. The model incorporated RSV-related outpatient visits, hospitalizations, and deaths. We collected data from clinical trials, peer-reviewed literature, and Colombian national health databases. Costs were analyzed from the perspective of the Colombian healthcare system, adjusted to 2024 USD, and we applied a 5% discount rate to both costs and outcomes. We used a willingness-to-pay (WTP) threshold of $ 7,491 per quality-adjusted life year (QALY). Sensitivity analyses, including one-way and probabilistic sensitivity analyses, were performed to evaluate the robustness of the results. Results Maternal RSVpreF vaccination was associated with a reduction of approximately 25,781 RSV-related cases and an increase of 2,315 QALYs. The incremental cost-effectiveness ratio (ICER) was $ 2,322 per QALY, well below the WTP threshold. Sensitivity analyses identified vaccine effectiveness and RSV hospitalization rates as the primary drivers of cost-effectiveness. The probabilistic sensitivity analysis showed an 87.8% probability of the intervention being cost-effective at the established WTP threshold. Conclusion Maternal RSVpreF vaccination is a cost-effective intervention in Colombia, leading to significant reductions in RSV-related morbidity, mortality, and economic burden on the healthcare system. The findings support the inclusion of maternal RSV vaccination in national immunization programs, promoting health equity in underserved communities.
Predicting the distribution of invasive species in urban mountain ecosystems is important for biodiversity conservation and effective management. We developed a sequential two-stage species distribution modelling framework to characterize habitat suitability for the invasive vine Thunbergia alata in the Metropolitan District of Quito and to evaluate the contribution of citizen science records, systematic field surveys, and multi-source environmental data. In the first stage, 637 iNaturalist occurrence records retained after quality control were combined with 12 bioclimatic variables from WorldClim to develop a preliminary MaxEnt model. The resulting habitat suitability map was classified into five categories and used to design a stratified field survey across the predicted suitability gradient. We sampled 1,009 locations, obtaining 450 confirmed presences and 559 independent absence records. In the second stage, the field-derived occurrence data were used to develop a refined MaxEnt model incorporating the bioclimatic variables together with elevation, slope, land cover, VIIRS nighttime lights, NDVI, and the red spectral band. Environmental predictors were standardized to a common 5m. spatial resolution and screened for multicollinearity. Both models were evaluated using receiver-operating characteristic analyses and AUC, with 10 replicate runs and 25% of occurrence records withheld for testing. Mean AUC values were 0.851 ± 0.012 and 0.902 ± 0.008 for the preliminary and refined models, respectively. In the preliminary model, habitat suitability was primarily associated with broad-scale climatic variables, particularly precipitation seasonality and temperature-related variables. In the refined model, nighttime light intensity and elevation showed the highest contributions and permutation importance, while NDVI and climatic variables also contributed to model predictions. Independent field validation showed that 47.2% of absence observations occurred in the two lowest suitability classes, whereas 33.5% occurred in areas classified as highly or very highly suitable. The refined model therefore captured substantial spatial variation in habitat suitability while also revealing areas of mismatch between predicted suitability and field observations. These results demonstrate the value of sequentially integrating citizen science, systematic field observations, and fine-resolution environmental predictors to improve invasive species mapping. This framework provides a practical approach for supporting targeted monitoring and management of invasive plants in complex urban mountain ecosystems.
Este artículo analiza los imaginarios sociales constituidos a través de las publicaciones en Instagram de los candidatos a las alcaldías de 19 municipios del Oriente antioqueño, Colombia, durante la campaña electoral local de 2023. El problema de investigación se inscribe en el campo de la comunicación política y se enfoca en comprender cómo los candidatos construyen y difunden imaginarios colectivos mediante plataformas digitales, y de qué manera estos imaginarios influyen en las prácticas electorales de la región. Partiendo del marco teórico de Cornelius Castoriadis sobre los imaginarios sociales, y mediante un enfoque cualitativo con análisis de contenido, se identificaron patrones en los formatos de las publicaciones, los actores representados, las acciones realizadas, los espacios, objetos, eslóganes y mensajes. Los resultados revelan que los candidatos usan los imaginarios sociales en sus publicaciones articulados en torno a cuatro ejes temáticos: la familia y la religiosidad, el empoderamiento femenino, el desarrollo y bienestar, y el campo y la ruralidad. La investigación concluye que Instagram opera como un dispositivo comunicativo que aporta a la construcción de imaginarios colectivos que moldean la percepción ciudadana sobre el liderazgo político y la visión del territorio.
Knowing the heritable fraction of productive traits, such as birth weight and weaning weight, is an indispensable tool for selecting animals with higher genetic merit, facilitating decision-making in crossbreeding programmes. This study aimed to genetically evaluate populations of Santa Inés sheep in Brazil and Colombia using Bayesian inference analysis. The study was conducted using information from three sheep farms. The first was located in the Municipality of La Ceja, Antioquia (Colombia), the second in the municipality of Barbosa, Antioquia (Colombia), and the third in the municipality of Campo Santana (Brazil). The model included the additive genetic effect and maternal genetic effect, and considered sex, delivery type, and contemporary group as fixed effects. Components of variance and genetic parameters were estimated. The mean and standard deviation for birth weight (BW) and weaning weight adjusted to 90 days (WW90) were 3.36 kilograms (kg) ± 0.8 and 3.19 kg ± 0.861 for Brazil, and 7.76 kg ± 4.76 and 20.18 kg ± 5.31 for Colombia. The mean of direct heritability (h²d) of BW and WW90 was 0.31 and 0.27 for Brazil, and 0.61 and 0.30 for Colombia. It can be concluded that it is essential to keep accurate records at all sheep farms to ensure a good selection of animals for breeding programmes, to ensure animals with greater genetic and productive potential.
