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Agriculture stands as one of the major economic pillars worldwide, with food production contributing significantly to income growth. However, agricultural activities also entail risks associated with uncontrollable factors along the supply chain. To address these challenges, mathematical models have been developed for forecasting crucial variables in managing agribusiness activities. In this context, this article employs a combination of systematic bibliometric analysis and the Latent Dirichlet Allocation (LDA) method, a semi-automated approach. The main objective of this study was to automate the identification of relevant topics and construct a bibliographic portfolio (BP) covering the period 2015–2022, focusing on methodologies used in articles and other bibliometric analyses. The 30 articles included in the BP address issues related to methodologies applied in the temporal analysis of agricultural commodities. These articles were categorized based on the nature of the prediction models used, classified as (i) machine learning (ML), (ii) machine learning and artificial neural networks (ML-NN), (iii) machine learning and ensemble (ML-Ensemble), (iv) machine learning and hybrid (ML-hybrid), and (v) statistical. Regarding the results, the topic that stood out the most was termed ''Forecasting Methods Applied to Agribusiness Time Series.'' The most utilized classes were ML-hybrid (41.95 %) and statistical (29.31 %), followed by ML-NN (14.94 %), ML (9.20 %), and ML-Ensemble (4.60 %) types. The theoretical contribution of this study lies in identifying literary gaps concerning forecasting methods applied to agribusiness, while its practical implication is to identify forecasting methodologies to support decision-making.

Introduction: Rosai-Dorfman Disease (RDD) is reactive histiocytic disorder, which rarely occurs in the breast. Typically, it presents in women during their 5th decade of life, for which it is often confused with malignancy. Very few reports have reported its occurrence in the younger population, particularly pediatric patients. Case presentation: A 13-year-old female presented with a painful 3-cm mass the upper quadrant of the left breast. Ultrasound showed a hypoechoic mass with irregular margins abutting the skin, reported as a BI-RADS 5 lesion. Excisional biopsy of the mass showed Rosai Dorfman disease of the breast with involved margins. Follow-up PET/CT did not show any residual FDG-avid regions in the breast. Conclusion: RDD should remain in the differential diagnosis for breast masses for teenage patients, as we report the youngest known case of RDD of the breast.

Summary: β-glucan is the major component of the extracellular matrix (ECM) of many fungi, including wood degrading fungi. Many of these species also secrete oxalate into the ECM. Our research demonstrates that β-glucan forms a novel, previously unreported, hydrogel at room temperature with oxalate. Oxalate was found to alter the rheometric properties of the β-glucan hydrogels, and modeling showed that β-glucan hydrogen bonds with oxalate in a non-covalent matrix. Change of oxalate concentration also impacted the diffusion of a high-molecular-weight protein through the gels. This finding has relevance to the diffusion of extracellular enzymes into substrates and helps to explain why some types of wood-decay fungi rely on non-enzymatic degradation schemes for carbon cycling. Further, this research has potential impact on the diffusion of metabolites in association with pathogenic/biomedical fungi.

Intussusception is the most common cause of intestinal obstruction among children up to three years of age. Though usually idiopathic, it may seldom be caused by a pathologic lead point. We present the case of a one-month-old female patient who was diagnosed with intussusception secondary to an ileal adenomyoma. To our knowledge, this case represents the youngest patient to be described in literature with an intussusception caused by a hamartoma.