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Beginning in late 2016 Brazil faced the worst outbreak of Yellow Fever in recent decades, mainly located in southeastern rural regions of the country. In the present study we characterize the Yellow Fever Virus (YFV) associated with this outbreak in São Paulo State, Brazil. Blood or tissues collected from 430 dead monkeys and 1030 pools containing a total of 5,518 mosquitoes were tested for YFV by quantitative RT-PCR, immunohistochemistry (IHC) and indirect immunofluorescence. A total of 67 monkeys were YFV-positive and 3 pools yielded YFV following culture in a C6/36 cell line. Analysis of five nearly full length genomes of YFV from collected samples was consistent with evidence that the virus associated with the São Paulo outbreak originated in Minas Gerais. The phylogenetic analysis also showed that strains involved in the 2016–2017 outbreak in distinct Brazilian states (i.e., Minas Gerais, Rio de Janeiro, Espirito Santo) intermingled in maximum-likelihood and Bayesian trees. Conversely, the strains detected in São Paulo formed a monophyletic cluster, suggesting that they were local-adapted. The finding of YFV by RT-PCR in five Callithrix monkeys who were all YFV-negative by histopathology or immunohistochemistry suggests that this YFV lineage circulating in Sao Paulo is associated with different outcomes in Callithrix when compared to other monkeys.

Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018–2021), and then increased in the northeast (2019–2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study’s results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected.

Sustainable agriculture relies on effective soil management, making it crucial to assess soil health, especially in areas of agricultural expansion, such as the Cerrado in the MATOPIBA region. Sustainable strategies, such as integrated production systems (crop–livestock–forestry), are essential to mitigate these impacts. However, little is known about the effects of these systems on soil microbial communities. The objective of this study was to evaluate bacterial communities associated with soils under different integrated production systems in the MATOPIBA region. Soil samples from the 0–10 cm depth layer were collected from the following land use systems: (i) native Cerrado vegetation (NCV), (ii) native Babassu forest (NPV), (iii) no-tillage soybean—regional standard system (NT-S), (iv) crop–forest integration (CFI), (v) crop–livestock integration (CLI), and (vi) livestock–forest integration (LFI). We measured chemical properties and bacterial communities using next-generation sequencing (NGS) of the V3-V4 hypervariable region of the 16S rRNA gene. The results revealed that the integration systems (CFI, CLI, and LFI) resulted in changes in soil chemical properties, which contributed to the modulation of the bacterial communities. The most abundant taxa in integrated production systems shows a positive correlation with soil pH and phosphorus content. Members of the Nitrosomonadaceae and Sphingomonadaceae families are more related to integrated production systems containing a forestry component (CFI and LFI), while Bacillaceae are more evident in crop–livestock integration systems (CLI).

Retinoblastoma was one of the first malignant tumors to be described as a genetic disease and its development occurs from the loss of function of the retinoblastoma gene (RB1). The difficulty in accessing the tumor during diagnosis highlights the need for non-invasive diagnostic methods. Studies have shown that liquid biopsy, obtained from any fluid material in the body, for example blood, contains free tumor cells and free and circulating DNA or RNA, making it a convenient tool for diagnosis and prognosis during cancer treatment without the need for invasive procedures. Taking advantage of these events, given this situation, we investigated molecular alterations in samples from retinoblastoma cases, using the NGS strategy as a powerful tool for characterization and aid in diagnosis and prognosis. Genomic data from 76 patients diagnosed with retinoblastoma, comprising 162 samples, tumor (TU), aqueous humor (AH), and peripheral blood (PB), were analyzed using the Oncomine Childhood Cancer Research Panel (OCCRA®). A total of 22 altered genes were detected, and 54 variants. Of the 76 cases, 29 included paired tumor (TU), aqueous humor (AH), and peripheral blood (PB) samples from the same patient. Alterations in the RB1 gene were detected in 16 of these 29 cases, with concordant alterations identified across all three sample types in three patients. In 12 out of 29 patients, the same genetic alteration was found in both TU and AH. In conclusion, the OCCRA panel enabled the detection, in different samples, of molecular alterations in the RB1 gene, as well as CNAs in the MYCN, ABL2, and MDM4 genes. Limitations of AH were observed, primarily due to the small volume of material available and the consequently low concentration of cell-free DNA (cfDNA). However, as AH provides a viable alternative for analyzing tumors, inaccessible to traditional biopsy methods, liquid biopsy holds significant potential to improve diagnostic accuracy and guide treatment strategies in retinoblastoma cases.

Retinoblastoma (RB) is the most common intraocular malignancy of childhood, yet molecular assessment of disease dissemination and minimal residual disease (MRD) remains challenging due to the contraindication of intraocular biopsy. Here, we evaluate the feasibility of cell-free RNA (cfRNA)- and circulating tumor cell RNA (ctcRNA)-based liquid biopsy for the sensitive detection of disseminated retinoblastoma using digital PCR (dPCR) targeting the retina-specific markers CRX and RBP3. We analyzed 433 bone marrow (BM), peripheral blood (PB) and cerebrospinal fluid (CSF) samples collected longitudinally from 50 patients with RB. dPCR assays demonstrated high analytical sensitivity. cfRNA detection showed complete sensitivity and negative predictive value in bone marrow compared with myelogram analysis, frequently identifying molecular positivity in cytologically negative samples. In cerebrospinal fluid, cfRNA detection was highly specific but less sensitive, reflecting compartment-specific biological constraints. Longitudinal analysis revealed that changes in CRX and RBP3 ctcRNA levels closely tracked treatment response, preceded cytological evidence of bone marrow involvement in several cases, and identified molecular persistence or re-emergence during follow-up, including after hematopoietic stem cell transplantation. Together, these findings demonstrate that cfRNA- and ctcRNA-based liquid biopsy using CRX and RBP3 enables sensitive and dynamic detection of disseminated retinoblastoma, particularly in bone marrow, and supports its potential utility for MRD monitoring. Longitudinal patient analyses will be required to define prognostic thresholds and establish the clinical role of this approach in risk stratification and long-term surveillance.

Solid dispersions (SD) are used as a technological strategy to increase the aqueous dissolution rate of poorly soluble active pharmaceutical ingredients, such as hydrochlorothiazide (HCTZ), an antihypertensive used frequently in medical clinics. The aim of this study was to characterize solid dispersions of HCTZ obtained with different processing adjuvants, using the DSC, TG, XRPD, FTIR and SEM techniques, and to evaluate the influence of carriers used in biopharmaceutical performance by analyzing dissolution efficiency. The SDs were obtained using the solvent method, and spray drying was used as the drying technique. The carriers used PEG 1500, sodium lauryl sulfate and PVP K30. The calorimetric analysis and XRPD showed amorphous behavior to SDs that used hydrophilic polymer as a carrier, and thermogravimetric analysis showed maintaining thermal stability of the HCTZ for most dispersions. FTIR detected intermolecular interactions of hydrogen bonds, while SEM showed the formation of microparticles with a tendency to sphericity. Acquired morphology associated with amorphization contributed to the increase in dissolution efficiency of dispersions, being that this SD (HCTZ/PVP K30) showed the best increase in dissolution. We therefore concluded that the analytical techniques used were of fundamental importance to the characterization of pharmaceutical products developed as to their physicochemical properties and the prescience of the oral bioavailability of HCTZ.

Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.

Sugarcane is a widely cultivated plant that serves primarily as a source of sugar and ethanol. Its annual yield can be significantly reduced by the action of several insect pests including the sugarcane giant borer (Telchin licus licus), a lepidopteran that presents a long life cycle and which efforts to control it using pesticides have been inefficient. Although its economical relevance, only a few DNA sequences are available for this species in the GenBank. Pyrosequencing technology was used to investigate the transcriptome of several developmental stages of the insect. To maximize transcript diversity, a pool of total RNA was extracted from whole body insects and used to construct a normalized cDNA database. Sequencing produced over 650,000 reads, which were de novo assembled to generate a reference library of 23,824 contigs. After quality score and annotation, 43% of the contigs had at least one BLAST hit against the NCBI non-redundant database, and 40% showed similarities with the lepidopteran Bombyx mori. In a further analysis, we conducted a comparison with Manduca sexta midgut sequences to identify transcripts of genes involved in digestion. Of these transcripts, many presented an expansion or depletion in gene number, compared to B. mori genome. From the sugarcane giant borer (SGB) transcriptome, a number of aminopeptidase N (APN) cDNAs were characterized based on homology to those reported as Cry toxin receptors. This is the first report that provides a large-scale EST database for the species. Transcriptome analysis will certainly be useful to identify novel developmental genes, to better understand the insect's biology and to guide the development of new strategies for insect-pest control.

Machine learning (ML) is a promising tool in assisting clinical decision-making for improving diagnosis and prognosis, especially in developing regions. It is often used with large samples, aggregating data from different regions and hospitals. However, it is unclear how this affects predictions in local centers. This study aims to compare data aggregation strategies of several hospitals in Brazil with a local training strategy in each hospital to predict two COVID-19 outcomes: Intensive Care Unit admission (ICU) and mechanical ventilation use (MV). The study included 6,046 patients from 14 hospitals, with local sample sizes ranging from 47 to 1500 patients. Machine learning models were trained using extreme gradient boosting, lightGBM, and catboost for structured data. Seven data aggregation strategies based on hospital geographic regions were compared with local training, and the best strategy was determined by analyzing the area under the ROC curve (AUROC). SHAP (Shapley Additive exPlanations) values were used to assess the contribution of variables to predictions. Additionally, a metafeatures analysis examined how hospital characteristics influence the selection of the best strategy. The study found that the local training strategy was the most effective approach, in the case of ICU outcomes, for 11 of the 14 hospitals (79%), and, in the case of MV, for 10 hospitals (71%). Metafeatures analysis suggested that hospitals with smaller sample sizes generally performed better using an aggregated data strategy compared to local training. Our study brings to light an important concern about the impact of grouping data from different hospitals in predictive machine learning models. These findings contribute to the ongoing debate about the trade-off between increasing sample size and bringing together heterogeneous scenarios.