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Objective: This work sought to identify the academic communities that have shown interest and participation in the Journal Research and Education in Nursing and analyze the scientific impact generated by said journal. Methods: A bibliometric analysis was carried out, as well as social network analysis and techniques of natural language processing to conduct the research. The data was gathered and analyzed during a specific study period, covering from 2010 - 2020, for articles published in the journal, and 2010 - 2022, for articles that cited the journal within Scopus. These methods permitted performing an exhaustive evaluation of the journal's influence and reach in diverse academic and geographic contexts. Results: During the analysis, it was noted that the journal Research and Education in Nursing has had significant influence in academic and scientific communities, both nationally and internationally. Collaboration networks were detected among diverse institutions and countries, which indicates active interaction in the field of nursing research. In addition, trends and emerging patterns were identified in this field, providing a more complete view of the discipline's evolution. Conclusion: Based on the results obtained, it is concluded that the journal Research and Education in Nursing has played un fundamental role in disseminating knowledge and promoting research in nursing. The combination of Bibliometric metrics, social network analysis, and natural language processing permitted utmost comprehension of its impact in the scientific and academic community globally.

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The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composition, and context of land covers. This study employed machine learning methods (partial least square regressor and the Shapley Additive exPlanations) to explore the intricate relationships between urban expansion, land cover changes, and water quality in a watershed with a park and lake. To address this, we first evaluated the spatio-temporal variation of some physicochemical and microbiological water quality variables, generated yearly land cover maps of the basin adopting several machine learning classifiers, and computed the most suitable landscape metrics that better represent the land cover. The main results highlighted the importance of spatial arrangement and the size of the contributing watershed on water quality. Compact urban forms appeared to mitigate the impact on pollutants. This research provides valuable insights into the intricate relationship between landscape characteristics and water quality dynamics, informing targeted watershed management strategies aimed at mitigating pollution and ensuring the health and resilience of aquatic ecosystems.

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From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

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Objectives. This work sought to identify the academic communities that have shown interest and participation in the Journal Research and Education in Nursing and analyze the scientific impact generated by said journal. Methods. A bibliometric analysis was carried out, as well as social network analysis and techniques of natural language processing to conduct the research. The data was gathered and analyzed during a specific study period, covering from 2010 - 2020, for articles published in the journal, and 2010 - 2022, for articles that cited the journal within Scopus. These methods permitted performing an exhaustive evaluation of the journal's influence and reach in diverse academic and geographic contexts. Results. During the analysis, it was noted that the journal Research and Education in Nursing has had significant influence in academic and scientific communities, both nationally and internationally. Collaboration networks were detected among diverse institutions and countries, which indicates active interaction in the field of nursing research. In addition, trends and emerging patterns were identified in this field, providing a more complete view of the discipline's evolution. Conclusion. Based on the results obtained, it is concluded that the journal Research and Education in Nursing has played un fundamental role in disseminating knowledge and promoting research in nursing. The combination of Bibliometric metrics, social network analysis, and natural language processing permitted utmost comprehension of its impact in the scientific and academic community globally.

Objetivos. Identificar las comunidades académicas que han mostrado interés y participación en la revista Investigación y Educación en Enfermería y analizar el impacto científico generado por esta publicación. Métodos. Se realizó un análisis bibliométrico, así como análisis de redes sociales y técnicas de procesamiento de lenguaje natural para llevar a cabo la investigación. Los datos se recopilaron y analizaron durante un período de estudio específico, abarcando los años 2010-2020, para los artículos publicados en la revista, y 2010-2022, para los artículos que citaron la revista dentro de Scopus. Estos métodos permitieron realizar una evaluación exhaustiva de la influencia y alcance de la revista en diversos contextos académicos y geográficos. Resultados. Durante el análisis, se observó que la revista Investigación y Educación en Enfermería ha ejercido una influencia significativa en las comunidades académicas y científicas, tanto a nivel nacional como internacional. Se detectaron redes de colaboración entre diversas instituciones y países, lo que indica una interacción activa en el ámbito de la investigación en enfermería. Además, se identificaron tendencias y patrones emergentes en este campo, proporcionando una visión más completa de la evolución de la disciplina. Conclusión. Basándose en los resultados obtenidos, se concluye que la revista Investigación y Educación en Enfermería ha desempeñado un papel fundamental en la difusión del conocimiento y la promoción de la investigación en enfermería. La combinación de métricas bibliométricas, análisis de redes sociales y procesamiento de lenguaje natural permitió una comprensión más completa de su impacto en la comunidad científica y académica a nivel global.

Objetivos. Identificar as comunidades acadêmicas que demonstraram interesse e participação na revista Nursing Research and Education e analisar o impacto científico gerado por esta publicação colombiana. Métodos. Foi realizada análise bibliométrica, análise de redes sociais e técnicas de processamento de linguagem natural para a realização da pesquisa. Os dados foram coletados e analisados durante um período específico de estudo, abrangendo os anos 2010-2020, para artigos publicados na revista, e 2010-2022, para artigos que citaram a revista dentro do Scopus. Esses métodos permitiram uma avaliação abrangente da influência e do alcance da revista em diversos contextos acadêmicos e geográficos. Resultados. Durante a análise, observou-se que a revista Nursing Research and Education tem exercido influência significativa nas comunidades acadêmica e científica, tanto nacional quanto internacionalmente. Foram detectadas redes de colaboração entre diversas instituições e países, o que indica interação ativa no campo da pesquisa em enfermagem. Além disso, foram identificadas tendências e padrões emergentes neste campo, proporcionando uma visão mais completa da evolução da disciplina. Conclusão. Com base nos resultados obtidos, conclui-se que a revista Nursing Research and Education tem desempenhado um papel fundamental na divulgação do conhecimento e na promoção da investigação em enfermagem. A combinação de métricas bibliométricas, análise de redes sociais e processamento de linguagem natural permitiu uma compreensão mais completa do seu impacto na comunidade científica e académica global.

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Research into microbial interactions during coffee processing is essential for developing new methods that adapt to climate change and improve flavor, thus enhancing the resilience and quality of global coffee production. This study aimed to investigate how microbial communities interact and contribute to flavor development in coffee processing within humid subtropical climates. Employing Illumina sequencing for microbial dynamics analysis, and high-performance liquid chromatography (HPLC) integrated with gas chromatography-mass spectrometry (GC-MS) for metabolite assessment, the study revealed intricate microbial diversity and associated metabolic activities. Throughout the fermentation process, dominant microbial species included Enterobacter, Erwinia, Kluyvera, and Pantoea from the prokaryotic group, and Fusarium, Cladosporium, Kurtzmaniella, Leptosphaerulina, Neonectria, and Penicillium from the eukaryotic group. The key metabolites identified were ethanol, and lactic, acetic, and citric acids. Notably, the bacterial community plays a crucial role in flavor development by utilizing metabolic versatility to produce esters and alcohols, while plant-derived metabolites such as caffeine and linalool remain stable throughout the fermentation process. The undirected network analysis revealed 321 interactions among microbial species and key substances during the fermentation process, with Enterobacter, Kluyvera, and Serratia showing strong connections with sugar and various volatile compounds, such as hexanal, benzaldehyde, 3-methylbenzaldehyde, 2-butenal, and 4-heptenal. These interactions, including inhibitory effects by Fusarium and Cladosporium, suggest microbial adaptability to subtropical conditions, potentially influencing fermentation and coffee quality. The sensory analysis showed that the final beverage obtained a score of 80.83 ± 0.39, being classified as a specialty coffee by the Specialty Coffee Association (SCA) metrics. Nonetheless, further enhancements in acidity, body, and aftertaste could lead to a more balanced flavor profile. The findings of this research hold substantial implications for the coffee industry in humid subtropical regions, offering potential strategies to enhance flavor quality and consistency through controlled fermentation practices. Furthermore, this study contributes to the broader understanding of how microbial ecology interplays with environmental factors to influence food and beverage fermentation, a topic of growing interest in the context of climate change and sustainable agriculture.

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Tropical forests are global biodiversity hotspots and are crucial in the global carbon (C) cycle. Understanding the drivers of aboveground carbon stock (AGC) in a heterogeneous and biodiverse system can shed light on the processes underlying the relationship between biodiversity and carbon accumulation. Here, we investigate how biodiversity, environment, and landscape structure affect AGC. We examined such associations in 349 plots comprising over 95,346 km2 the Atlantic Forest of southern Brazil, encompassing three forest types: Dense Ombrophylous Forest (DF), Mixed Ombrophylous Forest (MF), and Seasonal Deciduous Forest (SF). Each plot was described by environmental variables, landscape metrics, and biodiversity (species richness and functional diversity). We used diversity, environmental, and landscape variables to build generalized linear mixed models and understand which can affect the forest AGC. We found that species richness is associated positively with AGC in all forest types, combined and separately. Seasonal temperature and isothermality affect AGC in all forest types; additionally, stocks are positively influenced by annual precipitation in SF and isothermality in MF. Among landscape metrics, total fragment edge negatively affects carbon stocks in MF. Our results show the importance of species diversity for carbon stocks in subtropical forests. The climate effect was also relevant, showing the importance of these factors, especially in a world where climate change tends to affect forest stock capacity negatively.

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INTRODUCTION: Lung cancer resection has largely focused on perioperative outcomes (eg, mortality) to benchmark performance. While variations in perioperative outcomes and in utilization of services (eg, ambulatory procedures, hospitalization) have been independently demonstrated, there has been limited evaluation of associations between these outcomes. We evaluated the association between perioperative outcomes and utilization of services to evaluate provider performance across a broader context of care. PATIENTS AND METHODS: This was a retrospective cohort study of patients undergoing lung cancer resection in 2017 to 2019. We utilized hierarchical logistic regression models to determine risk- and reliability-adjusted mortality and risk-adjusted utilization of services, at the hospital-level. We then evaluated utilization of services across quartiles of perioperative mortality. RESULTS: A total of 15,168 patients across 297 hospitals underwent lung cancer resection. Mean risk- and reliability-adjusted 90-day mortality varied between 1.58% (95% CI, 1.54%-1.62%) and 2.74% (95% CI, 2.59%-2.90%) across quartiles. Risk-adjusted utilization of all ambulatory procedures was highest in the best performing (lowest mortality) quartile at 37.7% (95% CI, 33.6%-41.8%). Additionally, risk-adjusted inpatient utilization prior to and after surgery was lowest in the best performing quartile at 15% (95% CI, 13.7%-16.3%) and 19.3% (95% CI, 17.5%-21.0%), respectively. CONCLUSIONS: Hospitals with the lowest perioperative mortality demonstrated trends towards using more outpatient resources prior to surgery, but fewer inpatient services surrounding lung cancer resection. This correlation highlights the importance of incorporating utilization of services in addition to other metrics to profile the efficiency and effectiveness of centers performing lung cancer resection across a broader spectrum of care.

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BACKGROUND: Work-related musculoskeletal disorders for upper limbs (UL-WMSDs) form a complex of occupational diseases common to many professions worldwide. UL-WMSDs are manifested in most cases by pain, resulting in musculoskeletal discomfort. OBJECTIVE: This research aimed to evaluate the perception of musculoskeletal discomfort in workers from the interior of the Brazilian states of Alagoas and Bahia through the construction of a scale to assess musculoskeletal discomfort for upper limb. METHODS: The discomfort assessment scale was constructed from self-reported pain symptoms by 420 workers from the inner regions of the Brazilian states of Alagoas and Bahia. The reliability and dimensionality of the collected data were analyzed by McDonald's Omega and exploratory factor analysis, respectively. Item Response Theory (IRT) was used to create parameters for the discomfort scale. RESULTS: The musculoskeletal discomfort metric was constructed from the workers' response with six levels (varying from minimum discomfort to maximum discomfort). At the lowest level of the scale, workers indicated symptoms in the shoulders and wrists were rare. At the highest level of the scale, daily pain symptoms are reported in all regions of the upper limbs. The shoulders are the last region to develop extreme pain symptoms. CONCLUSION: The metric was created to present satisfactory psychometric properties and capable measurement of the workers' level of musculoskeletal discomfort based on self-reported pain symptoms. Therefore, the metric can support measuring discomfort, contributing to decisions that improve a healthier occupational environment for the worker.

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INTRODUCTION: ECLIM-SEHOP platform was created in 2017. Its main objective is to establish the infrastructure to allow Spanish participation into international academic collaborative clinical trials, observational studies, and registries in pediatric oncology. The aim of this manuscript is to describe the activity conducted by ECLIM-SEHOP since its creation. METHODS: The platform's database was queried to provide an overview of the studies integrally and partially supported by the organization. Data on trial recruitment and set-up/conduct metrics since its creation until November 2023 were extracted. RESULTS: ECLIM-SEHOP has supported 47 studies: 29 clinical trials and 18 observational studies/registries that have recruited a total of 5250 patients. Integral support has been given to 25 studies: 16 trials recruiting 584 patients and nine observational studies/registries recruiting 278 patients. The trials include front-line studies for leukemia, lymphoma, brain and solid extracranial tumors, and other key transversal topics such as off-label use of targeted therapies and survivorship. The mean time from regulatory authority submission to first patient recruited was 12.2 months and from first international site open to first Spanish site open was 31.3 months. DISCUSSION: ECLIM-SEHOP platform has remarkably improved the availability and accessibility of international academic clinical trials and has facilitated the centralization of resources in childhood cancer treatment. Despite the progressive improvement on clinical trial set-up metrics, timings should still be improved. The program has contributed to leveling survival rates in Spain with those of other European countries that presented major differences in the past.

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Landscape changes based on spectral responses allow showing plant cover changes through diversity, composition, and ecological connectivity. The spatial and temporal vegetation dynamics of the Bijagual Massif from 1986 to 2021 were analyzed as a measure of ecological integrity, conservation, and territory. The covers identified were high open forest (Hof), dense grassland of non-wooded mainland (Dgnm), a mosaic of pastures and crops (Mpc), lagoons (Lag), and bare and degraded lands (Bdl). The Bijagual Massif has 8574.1 ha. In 1986, Dgnm occupied 42.6% of the total area, followed by Mpc (32.8%) and Hof (24.5%); by 2000, Mpc and Hof increased (43.7 and 28.1%, respectively), while Dgnm decreased (28%); by 2021, Dgnm was restricted to the northeastern zone and continued to decrease (25.2%), Mpc occupied 52.9%, Hof 21.7% and Bdl 0.1%. Of the three fractions of the connectivity probability index, only dPCintra and dPCflux contribute to ecological connectivity. Hof and Dgnm show patches with biota habitat quality and availability. Between 1986 and 2021, Dgnm lost 1489 ha (41%) and Hof 239.5 ha (11%). Mpc replaced various covers (1722.2 ha; 38%) in 2021. Bijagual has a valuable biodiversity potential limited by Mpc. Territorial planning and sustainable agroecological and ecotourism proposals are required due to the context of the ecosystems.

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BACKGROUND: The Global Diet Quality Score (GDQS) was developed to be a simple, timely and cost-effective tool to track, simultaneously, nutritional deficiency and non-communicable disease risks from diet in diverse settings. The objective was to investigate the performance of GDQS as an indicator of adequate nutrient intake and dietary quality in a national-representative sample of the Brazilian population. METHODS: Nationally-representative data from 44,744 men and non-pregnant and non-lactating women aging ≥ 10 years, from the Brazilian National Dietary Survey were used. Dietary data were collected through two 24-h recalls (24HR). The GDQS was calculated and compared to a proxy indicator of nutrient adequate intake (the Minimum Dietary Diversity for Women-MDD-W) and to an indicator of high-risk diet for non-communicable diseases (caloric contribution from ultra-processed foods-UPF). To estimate the odds for overall nutrient inadequacy across MDD-W and GDQS quintiles, a multiple logistic regression was applied, and the two metrics' performances were compared using Wald's post-test. RESULTS: The mean GDQS for Brazilians was 14.5 (0-49 possible range), and only 1% of the population had a low-risk diet (GDQS ≥ 23). The GDQS mean was higher in women, elderly individuals and in higher-income households. An inverse correlation was found between the GDQS and UPF (rho (95% CI) = -0.20(-0.21;-0.19)). The odds for nutrient inadequacy were lower as quintiles of GDQS and MDD-W were higher (p-trend < 0.001), and MDD-W had a slightly better performance than GDQS (p-diff < 0.001). Having a low-risk GDQS (≥ 23) lowered the odds for nutrient inadequacy by 74% (95% CI:63%-81%). CONCLUSION: The GDQS is a good indicator of overall nutrient adequacy, and correlates well with UPF in a nationally representative sample of Brazil. Future studies must investigate the relationship between the GDQS and clinical endpoints, strengthening the recommendation to use this metric to surveillance dietary risks.

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AIM: To examine and compare the autonomic responses, as assessed through the non-linear and chaotic global metrics of heart rate variability in two groups: the Parkinson's Disease Group (PDG) and the Control Group (CG), both at rest and during an active tilt test. METHODS: The study encompassed 46 participants (PDG: n = 23; 73.73 ± 7.28 years old; CG: n = 23; 70.17 ± 8.20 years old). Initial data collection involved the acquisition of participant's characteristics. The autonomic modulation was estimated both at rest and during the active tilt test. For this assessment, we computed non-linear indices derived from five entropies (Approximate, Sample, Shannon, Renyi, Tsallis), Detrended Fluctuation Analysis and the seven chaotic global metrics (hsCFP1-hsCFP7). RESULTS: At rest, the PDG exhibited lower values of hsCFP3 (0.818 ± 0.116 vs. 0.904 ± 0.065; p < 0.05) and Sample Entropy (0.720 ± 0.149 vs. 0.799 ± 0.171; p < 0.05). During the test, the PDG demonstrated lower values of ApEn, while the CG presented lower values of SampEn, hsCFP1, hsCFP3, hsCFP7, and higher values of hsCFP5. An interaction was observed, indicating that hsCFP1 and hsCFP3 exhibit differential behavior for the CG and PDG in response to the test. CONCLUSION: subjects with PD exhibited reduced complexity of the RR interval series at rest, and a diminished autonomic response to the active tilt test when compared with the CG. The test, together with non-linear indices, may serve for assessing the Autonomic Nervous System in individuals with PD in a clinical setting. The interpretation of these data should be approached with caution, given the possible influences of pharmacotherapies and the inclusion of diabetic participants.

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The Paraná River Delta in South America, a large wetlands macromosaic, faces threats from climate change, human activities like livestock intensification, and hydrological modifications driven by the construction of water management infrastructure to prevent flooding in productive lands. Macroinvertebrates, essential for wetland health, are affected by cattle-induced changes in water quality, nutrient enrichment, and trampling, posing challenges to the ecosystem's ecological balance and long-term survival of these organisms. In this study, we analyzed the impact of two categories of cattle stocking rates (low and high) on the taxonomic and functional structure of the aquatic macroinvertebrate community in freshwater marshes. In addition, we compare the influence of cattle stocking rate on macroinvertebrates in natural and modified freshwater marshes, and, finally, the effect of cattle stocking rate in three contrasting hydrometeorological periods: a drier, a humid, and an extreme drought period. Samplings were conducted in 16 freshwater habitats of the Lower Paraná River Delta, examining variables such as temperature, pH, dissolved oxygen, coliforms, and nutrient concentrations. Macroinvertebrates were collected and functional and taxonomic metrics were estimated. Statistical analyses, including ANOVA and Kruskal-Wallis tests, were conducted to evaluate the effects of cattle stocking rates, hydrological modifications, and hydrometeorological periods on macroinvertebrate metrics and environmental variables. RDA, PERMANOVA, and SIMPER analyses explored the relationships between assemblage composition and environmental factors. High stocking rate altered the community structure, modifying its composition and decreasing the density, taxonomic and functional richness. Moreover, hydrological alterations exacerbated these negative impacts of cattle overstocking in macroinvertebrates. Under severe drought conditions, only tolerant species can survive cattle overstocking conditions. Our findings provide relevant insight into the ecological risks associated with cattle overstocking in natural and modified freshwater marshes and underscore the need to control cattle stocking rates in extreme drought to avoid loss of ecological functions.

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Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

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The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

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Eye tracking has become increasingly applied in medical education research for studying the cognitive processes that occur during the performance of a task, such as image interpretation and surgical skills development. However, analysis and interpretation of the large amount of data obtained by eye tracking can be confusing. In this article, our intention is to clarify the analysis and interpretation of the data obtained from eye tracking. Understanding the relationship between eye tracking metrics (such as gaze, pupil and blink rate) and cognitive processes (such as visual attention, perception, memory and cognitive workload) is essential. The importance of calibration and how the limitations of eye tracking can be overcome is also highlighted.

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BACKGROUND: Nutrition risk is prevalent in intensive care unit (ICU) settings and related to poor prognoses. We aimed to evaluate the concurrent and predictive validity of different nutrition risk screening tools in the ICU. METHODS: Data were collected between 2019 and 2022 in six ICUs (n = 450). Nutrition risk was evaluated by modified Nutrition Risk in Critically ill (mNUTRIC), Nutritional Risk Screening (NRS-2002), Malnutrition Screening Tool (MST), Malnutrition Universal Screening Tool (MUST), and Nutritional Risk in Emergency (NRE-2017). Accuracy and agreement of the tools were assessed; logistic regression was used to verify the association between nutrition risk and prolonged ICU stay; Cox regression was used for mortality in the ICU, both with adjustment for confounders. RESULTS: NRS-2002 ≥5 showed the best accuracy (0.63 [95% CI, 0.58-0.69]) with mNUTRIC, and MST with NRS-2002 ≥5 (0.76 [95% CI, 0.71-0.80]). All tools had a poor/fair agreement with mNUTRIC (k = 0.019-0.268) and moderate agreement with NRS-2002 ≥5 (k = 0.474-0.503). MUST (2.26 [95% CI 1.40-3.63]) and MST (1.69 [95% CI, 1.09-2.60]) predicted death in the ICU, and the NRS-2002 ≥5 (1.56 [95% CI 1.02-2.40]) and mNUTRIC (1.86 [95% CI, 1.26-2.76]) predicted prolonged ICU stay. CONCLUSION: No nutrition risk screening tool demonstrated a satisfactory concurrent validity; only the MUST and MST predicted ICU mortality and the NRS-2002 ≥5 and mNUTRIC predicted prolonged ICU stay, suggesting that it could be appropriate to adopt the ESPEN recommendation to assess nutrition status in patients with ≥48 h in the ICU.

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In Brazil, lack of quality in the delivery of prenatal care is a persistent concern. In this study, I analyze the dynamics taking place in the prenatal clinical encounter, and illuminate how the requirement to produce metrics through registration and monitoring endorses a form of bureaucratic care. This form of care develops in a context characterized by scarcity and a lack of medical resources, where healthcare professionals attempt to contain uncertainty. Ruled by notions of risk, centered in measuring practices, and saturated by an overvaluation of technology, bureaucratic care reinforces the disenfranchizement and stigmatization of Black rural women.

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Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

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In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.