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The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.
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Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.
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BACKGROUND: Wealth mobility, as both relative (positional) and absolute (material) wealth acquisition, may counteract negative consequences of early life adversities on adult health. METHODS: We use longitudinal data (1967-2018) from the INCAP birth cohort, Guatemala (n = 1386). Using wealth as a measure of socio-economic position, we assess the association of life course relative mobility using latent class analysis and absolute material gains using conditional wealth measures. We estimate associations of wealth mobility with indicators of human capital, specifically height, weight status (BMI in kg/m2), psychological distress (WHO SRQ-20 score) and fluid intelligence (Ravens Progressive Matrices score; RPM) in middle adulthood. RESULTS: We identified four latent classes of relative mobility - Stable Low (n = 498), Stable High (n = 223), Downwardly Mobile (n = 201) and Upwardly Mobile (n = 464). Attained schooling (years) was positively associated with membership in Upwardly Mobile (odds ratio; 1.50, 95%CI: 1.31, 1.71) vs Stable Low, and inversely with membership in Downwardly Mobile (0.65, 95%CI: 0.54, 0.79) vs Stable High. Being Upwardly Mobile (vs Stable Low) was positively associated with height (1.88 cm, 95%CI: 1.04, 2.72), relative weight (1.32 kg/m2, 95%CI: 0.57, 2.07), lower psychological distress (-0.82 units, 95%CI: 1.34, -0.29) and fluid intelligence (0.94 units, 95%CI: 0.28, 1.59). Being Downwardly Mobile (vs Stable High) was associated with lower fluid intelligence (-2.69 units, 95%CI: 3.69, -1.68), and higher psychological distress (1.15 units, 95%CI: 0.34, 1.95). Absolute wealth gains (z-scores) from early to middle adulthood were positively associated with relative weight (0.62 kg/m2, 95%CI: 0.28, 0.96), lower psychological distress (-0.37 units, 95%CI: 0.60, -0.14) and fluid intelligence (0.50 units, 95%CI: 0.21, 0.79). CONCLUSIONS: Higher attained schooling provided a pathway for upward relative mobility and higher absolute wealth gains as well as protection against downward relative mobility. Upward mobility was associated with lower psychological distress and higher fluid intelligence but also higher weight status.
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Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R2 = 0.91) with a 45-sample training set and a good quality of prediction (R2 = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.
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Elicitation of cell suspensions culture is a strategy that could increase the production of secondary metabolites under controlled conditions. This research evaluated the effect of methyl jasmonate-MeJA and salicylic acid-SA as elicitors on the production of metabolites in cell suspensions of P. cumanense. The type of elicitor (MeJA or SA), the concentration of elicitor (10⯵M and 100⯵M), and time of exposition (3, 12, 24â¯h) on cell suspension were evaluated. Metabolic profiles of intracellular and extracellular extracts were analyzed by UHPLC-DAD and GC-MS. Differential production of metabolites was dependent on the type of elicitor, its concentration, and the time of exposition. Treatments with 100 µM SA were conducted to high production of 5-hydroxymethylfurfural (6.3 %), phenol (6.5 %), and (Z)-9-octadecenamide (8.8 %). This is the first report of elicitation on cell suspensions in the Piper genus and contributes to understanding the effect of MeJA and SA on metabolite production in plant cell culture.
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Plant cell suspension culture of T. peruviana is a feasible biotechnological platform for the production of secondary metabolites with anti-proliferative/cytotoxic activity, as phenolic compounds (PC); however, different in in vitro growth conditions may affect the production, demanding strategies to increase the metabolite biosynthesis, as well as the development of sensitive and rapid analytical methods for metabolite monitoring. The Fourier transform near-infrared (FT-NIR) spectroscopy and Reversed-phase high-performance liquid chromatography (RP-HPLC) combined with Multivariate analysis (MVA) were used to detect significant differences in the PC production in cultures treated with two elicitors. The results suggest that the FT-NIR-MVA is useful for discriminating samples according to the treatment, showed significant influence of the PC signal. RP-HPLC-MVA showed that the elicitor effect occurs at 72â¯h post-elicitation. Detection of dihydroquercetin (maximum concentrationâ¯=â¯12.59â¯mg/L), a flavonoid with anti-cancer properties, is highlighted. Future studies will be aimed at scaling this culture to increase the productivity of dihydroquercetin.
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Tamarind fruits are consumed worldwide and their seeds have an underexploited potential. We assessed the effect of the addition of a freeze-dried aqueous of extract tamarind seed (FAE) at three concentration levels (0.3, 1.15 and 2%) on the antioxidant capacity (DPPH, ABTS, FRAP and ORAC) and concentrations of total phenolic compounds in tamarind pulp. Conditions used to prepare the aqueous extracts were established using multivariate optimization. Moreover, nectars prepared from pulps combined with FAE were subjected to sensory tests. Tamarind fruits from three geographic regions in Brazil (Minas Gerais, São Paulo and Bahia) that were harvested in 2013 and 2014 were used in the study. Generally, the freeze-dried aqueous extracts increased the concentrations of antioxidants in the pulp. The results revealed a positive correlation between the FAE concentration in the pulp and the antioxidant capacity of all samples, particularly samples from Bahia and Minas Gerais, which presented an increase of up to 1,942% in the ABTS method when 2% FAE was incorporated into the pulp, from approximately 40.1 to 209.1 mMTrolox/gdw and 13.4 to 143.4 mMTrolox/gdw, respectively. Sensory tests indicated the satisfactory acceptance and non-distinction between nectar samples to which FAE was or was not added when the FAE concentration was less than 2.3gFAE/L, regardless of the geographic origin of the samples.
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Tamarindus , Antioxidantes , Brasil , Frutas , Extratos Vegetais , Néctar de PlantasRESUMO
BACKGROUND AIMS: Fourier Transform Infrared Micro-spectroscopy (FTIRM) is an emerging tool that obtains images with biochemical information of samples that are too small to be chemically analyzed by conventional Fourier transform infrared (FTIR) spectroscopy techniques. So, the central objective of this project was to study the biochemical similarity between articular and cultured chondrocytes by chemometric analysis from FTIRM. METHODS: Nine samples of knee articular cartilage were obtained; each sample was divided into two fragments, one portion was used for FTIRM characterization in situ, and from another part, chondrocytes were obtained to be cultured (in vitro), which were subjected to an FTIRM to characterize their biomolecular components. The FTIRM spectra were normalized, and the second derivative was calculated. From these data, principal component analysis (PCA) and a chemometric comparison between in situ and cultured chondrocytes were carried out. Finally, the biochemical mapping was conducted obtaining micro-FTIR imaging. RESULTS: FTIRM spectra of in situ and in vitro chondrocytes were obtained, and different biomolecules were detected, highlighting lipids, proteins, glycosaminoglycans, collagen, and aggrecan. Despite slight differences in the FTIR spectra, the PCA proved the organic similarity between in situ chondrocytes and cultured chondrocytes, which was also observed in the analysis of the ratios related to the degradation of the articular cartilage and collagen. In the same way, the ability of the FTIRM to characterize the molecular biodistribution was demonstrated. CONCLUSION: The biochemical composition and biodistribution analysis using FTIRM have been useful for comparing cultured chondrocytes and in situ chondrocytes.
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The genetic and morphometric population structures of the semiterrestrial crab Armases angustipes from along the Brazilian coast were examined. The influence of the Central South Equatorial Current on larval dispersal of A. angustipes also was evaluated. Six populations were sampled from estuarine areas in São Luis do Maranhão, Maranhão; Natal, Rio Grande do Norte; Maceió, Alagoas; Ilhéus, Bahia; Aracruz, Espírito Santo; and Guaratuba, Paraná. Patterns of genetic differentiation were assessed using DNA sequence data corresponding to parts of the mitochondrial cytochrome c oxidase subunit 1. Geometric morphometric techniques were used to evaluate morphological variation in shape and size of the carapace and right cheliped propodus. Our results revealed low genetic variability and lack of phylogeographic structure; geometric morphometrics showed statistically significant morphological differentiation and geographic structuring. Our data indicate the absence of possible barriers to gene flow for this mobile species, and no clear correlation of morphological or genetic variation with ocean currents and/or geographic distance. Our results also suggest that historical geological and climatological events and/or possible bottleneck effects influenced the current low genetic variability among the populations of A. angustipes.