Explore the vast range of titles available.

Zeolites are porous minerals with high absorbency and ion-exchange capacity. Their molecular structure is a dense network of AlO4 and SiO4 that generates cavities where water and other polar molecules or ions are inserted/exchanged. Even though there are several synthetic or natural occurring species of zeolites, the most widespread and studied is the naturally occurring zeolite clinoptilolite (ZC). ZC is an excellent detoxifying, antioxidant and anti-inflammatory agent. As a result, it is been used in many industrial applications ranging from environmental remediation to oral applications/supplementation in vivo in humans as food supplements or medical devices. Moreover, the modification as micronization of ZC (M-ZC) or tribomechanically activated zeolite clinoptilolite (TMAZ) or furthermore as double tribomechanically activated zeolite clinoptilolite (PMA-ZC) allows improving its benefits in preclinical and clinical models. Despite its extensive use, many underlying action mechanisms of ZC in its natural or modified forms are still unclear, especially in humans. The main aim of this review is to shed light on the geochemical aspects and therapeutic potentials of ZC with a vision of endorsing further preclinical and clinical research on zeolites, in specific on the ZC and its modified forms as a potential agent for promoting human brain health and overall well-being.

Gynostemma pentaphyllum (var. Ginpent) (GP) is a variety of Cucurbit with anti-inflammatory and antioxidant effects in patients. In this manuscript, the main components present in the dry extract of GP have been identified using Ultra High Performance Liquid Chromatography quadrupole-time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). In addition, the anti-inflammatory action of GP was evaluated in animal models with acute peripheral inflammation and motor alteration induced by lipopolysaccharide. The results showed that GP dry extract is rich in secondary metabolites with potential antioxidant and anti-inflammatory properties. We found that the treatment with GP induced a recovery of motor function measured with the rotarod test and pole test, and a reduction in inflammatory cytokines such as interleukin-1β and interleukin-6 measured with the ELISA test. The data collected in this study on the effects of GP in in vivo models may help integrate the therapeutic strategies of inflammatory-based disorders.

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software. Different calls typologies exist, and each ultrasonic call can be manually classified, but the qualitative analysis is highly time-consuming. Considering this framework, in this work we proposed and evaluated a set of supervised learning methods for automatic USVs classification. This could represent a sustainable procedure to deeply analyze the ultrasonic communication, other than a standardized analysis. We used manually built datasets obtained by segmenting the USVs audio tracks analyzed with the Avisoft software, and then by labelling each of them into 10 representative classes. For the automatic classification task, we designed a Convolutional Neural Network that was trained receiving as input the spectrogram images associated to the segmented audio files. In addition, we also tested some other supervised learning algorithms, such as Support Vector Machine, Random Forest and Multilayer Perceptrons, exploiting informative numerical features extracted from the spectrograms. The performance showed how considering the whole time/frequency information of the spectrogram leads to significantly higher performance than considering a subset of numerical features. In the authors’ opinion, the experimental results may represent a valuable benchmark for future work in this research field.

Ultrasonic vocalizations (USVs) are a major tool for assessing social communication in laboratory mice during their entire lifespan. At adulthood, male mice preferentially emit USVs towards a female conspecific, while females mostly produce ultrasonic calls when facing an adult intruder of the same sex. Recent studies have developed several sophisticated tools to analyze adult mouse USVs, especially in males, because of the increasing relevance of adult communication for the behavioral phenotyping of mouse models of autism spectrum disorder (ASD). Little attention has been instead devoted to adult female USVs and the impact of sex differences on the quantitative and qualitative characteristics of mouse USVs. Most of the studies have also focused on a single testing session, often without the concomitant assessment of other social behaviors (e.g., sniffings), so that little is still known about the link between USVs and other aspects of social interaction and their stability/variations across multiple encounters. Here we evaluated the USVs emitted by adult male and female mice during 3 repeated encounters with an unfamiliar female, with equal or different pre-testing isolation periods between sexes. We demonstrated clear sex differences in several USVs’ characteristics and other social behaviors and these were mostly stable across encounters and independent of pre-testing isolation. The estrous cycle of tested females exerted quantitative effects on their vocal and non-vocal behaviors, although it did not affect the qualitative composition of ultrasonic calls. Our findings obtained in B6 mice, i.e., the strain most widely used for the engineering of transgenic mouse lines, contribute to provide new guidelines for assessing ultrasonic communication in male and female adult mice.

Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls. Recently, many automated systems have been proposed for automatically performing both the detection and the classification of the USVs. Of course, the USV segmentation represents the crucial step for the general framework, since the quality of the call processing strictly depends on how accurately the call itself has been previously detected. In this paper, we investigate the performance of three supervised deep learning methods for automated USV segmentation: an Auto-Encoder Neural Network (AE), a U-NET Neural Network (UNET) and a Recurrent Neural Network (RNN). The proposed models receive as input the spectrogram associated with the recorded audio track and return as output the regions in which the USV calls have been detected. To evaluate the performance of the models, we have built a dataset by recording several audio tracks and manually segmenting the corresponding USV spectrograms generated with the Avisoft software, producing in this way the ground-truth (GT) used for training. All three proposed architectures demonstrated precision and recall scores exceeding 90 % , with UNET and AE achieving values above 95 % , surpassing other state-of-the-art methods that were considered for comparison in this study. Additionally, the evaluation was extended to an external dataset, where UNET once again exhibited the highest performance. We suggest that our experimental results may represent a valuable benchmark for future works.

Background: Rice (Oryza sativa L.) is the main food source for more than half of humankind. Rice is rich in phytochemicals and antioxidants with several biological activities; among these compounds, the presence of γ-oryzanol is noteworthy. The present study aims to explore the effects of γ-oryzanol on cognitive performance in a mouse model of neuroinflammation and cognitive alterations. Methods: Mice received 100 mg/kg γ-oryzanol (ORY) or vehicle once daily for 21 consecutive days and were then exposed to an inflammatory stimulus elicited by lipopolysaccharide (LPS). A novel object recognition test and mRNA expression of antioxidant and neuroinflammatory markers in the hippocampus were evaluated. Results: ORY treatment was able to improve cognitive performance during the neuroinflammatory response. Furthermore, phase II antioxidant enzymes such as heme oxygenase-1 (HO-1) and NADPH-dehydrogenase-quinone-1 (NQO1) were upregulated in the hippocampi of ORY and ORY+LPS mice. Lastly, γ-oryzanol showed a strong anti-inflammatory action by downregulating inflammatory genes after LPS treatment. Conclusion: These results suggest that chronic consumption of γ-oryzanol can revert the LPS-induced cognitive and memory impairments by promoting hippocampal antioxidant and anti-inflammatory molecular responses.

Autism spectrum disorder (ASD) is a neurodevelopmental condition mainly characterized by social impairments and repetitive behaviors. An altered intestinal barrier morphology and increased transmucosal leaks have also been implicated in ASD; in fact, comorbidities such as gastrointestinal problems (leaky gut) have frequently been reported in these patients. The regulation of tight junctions (TJs) is essential in maintaining intestinal barrier morphology and in regulating the delicate balance of trafficking between the intestinal lumen and the submucosa. To date, there are no definitive treatments for ASD comorbidities; however, melatonin (MLT) represents a well-validated and tolerated treatment for sleep disorders in ASD patients. The potential beneficial effects of MLT on this disorder have been and continue to be better investigated. In this context, the present study examines the effects of oral MLT administration (10 mg/kg/day for 16 weeks) on the intestinal barrier in BTBR T + Itpr3tf/J (BTBR) mice, a validated ASD model. Morphological analyses of the ileum of these animals reveal modified villus height (Vh), crypt depth (Cd), and Vh–Cd ratios; an inflammatory state; and a decrease in Paneth cells. Moreover, these mice showed altered TJ expression compared to the control animals (C57BL6/J mice). Notably, MLT normalizes morphological indices and TJ expression, consistent with an improved gut barrier morphology. These data collectively suggest that orally administered MLT can promote the remodeling of the intestinal barrier; thus, we can suppose that MLT reduces gastrointestinal barrier leaks. The overall safety and economy of MLT use suggest that this indolamine could be efficacious as an adjuvant therapy to reduce the condition known as leaky gut.

The approval and granting of marketing authorization for a putative biosimilar is based on strong comparability studies with its biological reference product. This is due to the complexity of the structure and nature of the manufacturing process of biological drugs. Hence, a rigorous analytical workflow for chemical characterization and clinical trials to evaluate the efficacy and safety is required to demonstrate their high similarities to the reference drug. This work is focused on the comparison of the originator of filgrastim with three of its biosimilars by evaluating their structural similarity and biological activity. Qualiquantitative analyses were performed by MALDI-TOF/TOF-MS and RP-HPLC-UV. An innovative functional assay using zebrafish as the animal model was developed to evaluate the biological activities of the drugs. The different analyses performed in this study highlighted the structural similarity of biosimilar drugs and their originator. This result was further confirmed by a similar in vivo biological activity.