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Keratocyst is known to be a benign lesion with aggressive behavior, rapid growth and a high recurrence rate. They are commonly asymptomatic, except when there is significant bone expansion with deformation and compression of the tissues surrounding the lesion. The objective of this report is to describe the surgical treatment of keratocyst in atrophic mandible. [ABSTRACT FROM AUTHOR]

Background: Maternally derived thyroid hormone (T3) is a fundamental factor for vertebrate neurodevelopment. In humans, mutations on the thyroid hormones (TH) exclusive transporter monocarboxylic acid transporter 8 (MCT8) lead to the Allan-Herndon-Dudley syndrome (AHDS). Patients with AHDS present severe underdevelopment of the central nervous system, with profound cognitive and locomotor consequences. Functional impairment of zebrafish T3 exclusive membrane transporter Mct8 phenocopies many symptoms observed in patients with AHDS, thus providing an outstanding animal model to study this human condition. In addition, it was previously shown in the zebrafish mct8 KD model that maternal T3 (MTH) acts as an integrator of different key developmental pathways during zebrafish development. Methods: Using a zebrafish Mct8 knockdown model, with consequent inhibition of maternal thyroid hormones (MTH) uptake to the target cells, we analyzed genes modulated by MTH by qPCR in a temporal series from the start of segmentation through hatching. Survival (TUNEL) and proliferation (PH3) of neural progenitor cells (dla, her2) were determined, and the cellular distribution of neural MTH-target genes in the spinal cord during development was characterized. In addition, in-vivo live imaging was performed to access NOTCH overexpression action on cell division in this AHDS model. We determined the developmental time window when MTH is required for appropriate CNS development in the zebrafish; MTH is not involved in neuroectoderm specification but is fundamental in the early stages of neurogenesis by promoting the maintenance of specific neural progenitor populations. MTH signaling is required for developing different neural cell types and maintaining spinal cord cytoarchitecture, and modulation of NOTCH signaling in a non-autonomous cell manner is involved in this process. Discussion: The findings show that MTH allows the enrichment of neural progenitor pools, regulating the cell diversity output observed by the end of embryogenesis and that Mct8 impairment restricts CNS development. This work contributes to the understanding of the cellular mechanisms underlying human AHDS. [ABSTRACT FROM AUTHOR]

Introduction: Bees capable of performing floral sonication (or buzz-pollination) are among the most effective pollinators of blueberries. However, the quality of pollination provided varies greatly among species visiting the flowers. Consequently, the correct identification of flower visitors becomes indispensable to distinguishing the most efficient pollinators of blueberry. However, taxonomic identification normally depends on microscopic characteristics and the active participation of experts in the decision-making process. Moreover, the many species of bees (20,507 worldwide) and other insects are a challenge for a decreasing number of insect taxonomists. To overcome the limitations of traditional taxonomy, automatic classification systems of insects based on Machine-Learning (ML) have been raised for detecting and distinguishing a wide variety of bioacoustic signals, including bee buzzing sounds. Despite that, classical ML algorithms fed by spectrogramtype data only reached marginal performance for bee ID recognition. On the other hand, emerging systems from Deep Learning (DL), especially Convolutional Neural Networks (CNNs), have provided a substantial boost to classification performance in other audio domains, but have yet to be tested for acoustic bee species recognition tasks. Therefore, we aimed to automatically identify blueberry pollinating bee species based on characteristics of their buzzing sounds using DL algorithms. Methods: We designed CNN models combined with Log Mel-Spectrogram representations and strong data augmentation and compared their performance at recognizing blueberry pollinating bee species with the current state-of-the-art models for automatic recognition of bee species. Results and Discussion: We found that CNN models performed better at assigning bee buzzing sounds to their respective taxa than expected by chance. However, CNN models were highly dependent on acoustic data pre-training and data augmentation to outperform classical ML classifiers in recognizing bee buzzing sounds. Under these conditions, the CNN models could lead to automating the taxonomic recognition of flower-visiting bees of blueberry crops. However, there is still room to improve the performance of CNN models by focusing on recording samples for poorly represented bee species. Automatic acoustic recognition associated with the degree of efficiency of a bee species to pollinate a particular crop would result in a comprehensive and powerful tool for recognizing those that best pollinate and increase fruit yields. [ABSTRACT FROM AUTHOR]

The hitherto implemented Listeria monocytogenes detection techniques are cumbersome or require expensive non-portable instrumentation, hindering their transposition into on-time surveillance systems. The current work proposes a novel integrated system resorting to loop-mediated isothermal amplification (LAMP), assisted by a bacteriophage P100–magnetic platform, coupled to an endpoint electrochemical technique, towards L. monocytogenes expeditious detection. Molybdophosphate-based optimization of the bacterial phagomagnetic separation protocol allowed the determination of the optimal parameters for its execution (pH 7, 25 °C, 32 µg of magnetic particles; 60.6% of specific capture efficiency). The novel LAMP method targeting prfA was highly specific, accomplishing 100% inclusivity (for 61 L. monocytogenes strains) and 100% exclusivity (towards 42 non-target Gram-positive and Gram-negative bacteria). As a proof-of-concept, the developed scheme was successfully validated in pasteurized milk spiked with L. monocytogenes. The phagomagnetic-based approach succeeded in the selective bacterial capture and ensuing lysis, triggering Listeria DNA leakage, which was efficiently LAMP amplified. Methylene blue-based electrochemical detection of LAMP amplicons was accomplished in 20 min with remarkable analytical sensitivity (1 CFU mL−1). Hence, the combined system presented an outstanding performance and robustness, providing a 2.5 h-swift, portable, cost-efficient detection scheme for decentralized on-field application. [ABSTRACT FROM AUTHOR]

With this 24-months stability, we believe that the gut, circulating (blood), and oral microbiomes of LKD may have limited impact to assess long-term risks of cardiovascular and kidney disease in LKD. Changes in the post-transplantation gut microbiome have been described in kidney transplant recipients (KTR), namely a decrease in overall microbial diversity and an increase in Enterobacteriaceae.[[1]] In addition, gut microbiome may influence tacrolimus dosage, and, in a bidirectional relation, the immunosuppressive therapy and the infection and rejection episodes may alter gut microbiome.[3] In the last decade, several alerts have been raised on the long-term risks of living kidney donors (LKD) regarding kidney and cardiovascular disease.[4] While considering these warnings, additional tools should be explored to help to assess these risks in LKD. [Extracted from the article]

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Vascular calcification (VC) is a frequent condition in chronic kidney disease (CKD) and a well-established risk factor for the development of cardiovascular disease (CVD). Gut dysbiosis may contribute to CVD and inflammation in CKD patients. Nonetheless, the role of gut and blood microbiomes in CKD-associated VC remains unknown. Therefore, this pilot study aimed to explore the link between gut and blood microbiomes and VC in CKD patients on peritoneal dialysis (CKD-PD). Our results showed relative changes in specific taxa between CKD-PD patients with and without VC, namely Coprobacter, Coprococcus 3, Lactobacillus, and Eubacterium eligens group in the gut, and Cutibacterium, Pajaroellobacter, Devosia, Hyphomicrobium, and Pelomonas in the blood. An association between VC and all-cause mortality risk in CKD-PD patients was also observed, and patients with higher mortality risk corroborate the changes of Eubacterium eligens in the gut and Devosia genus in the blood. Although we did not find differences in uremic toxins, intestinal translocation markers, and inflammatory parameters among CKD-PD patients with and without VC, soluble CD14 (sCD14), a nonspecific marker of monocyte activation, positively correlated with VC severity. Therefore, gut Eubacterium eligens group, blood Devosia, and circulating sCD14 should be further explored as biomarkers for VC, CVD, and mortality risk in CKD. [ABSTRACT FROM AUTHOR]

Providing resources to meet user needs in futuristic mobile networks is still challenging since the network resources like spectrum and base stations do not increase in the same proportion as the accelerated growth of network traffic. Because of this, human/user behavior attributes can assist resource management in dealing with these challenges, which pick up aspects of how the user impacts the usage of mobile networks, such as network usage, the content of interest, urban mobility routines, social networks, and sentiment. A user profile is a combination of user/human behavior attributes. Such profiles are expected to be a knowledge for softwarization enablers to improve the management of future wireless networks fully. Nevertheless, the correlation between human sentiment and wireless and mobile network usage has not been deeply investigated in the literature about the mobile user profile. This work aims to define the user profile using a transfer learning approach for the sentiment classification of WhatsApp messages. A real-life experiment was conducted to collect users' attributes, namely the WhatsApp messages and network usage. A new data analysis methodology is proposed that consists of a frequent item-set pattern mining (FP-Growth) based on Association Rules, the Chi-squared statistical test, and descriptive statistics. This methodology assesses the correlation between sentiment and network usage in a profound way. Results show that the users participating in the experiment form three groups. The first group, with 55.6% of the users, contains users who present a strong relation between negative sentiment and low network usage and also a strong relation between positive sentiment and high network usage. The second group contains 25.9% of the users and is composed of users who present a strong relation between positive sentiment and high network usage. The third group contains 18.5% of the users for whom the correlation between sentiment and network usage is still statistical significant, but the strength of this relation is much more weak then in the other two groups. Thus, 81.5% of the users (the first two groups) present a strong relation between user sentiment captured from WhatsApp messages and the network traffic generated by them. [ABSTRACT FROM AUTHOR]