Notes

Slim along with over weight microbiota: differences in within vitro fermentation associated with food-by-products.
Standard treatment consisting of chemoradiotherapy followed by radical surgery with total mesorectal excision, results in good oncologic local control but high morbidity and poor functional results. Since chemoradiotherapy results in 15% pathological complete response, even reaching up to 30% in case of association with neoadjuvant chemotherapy, radical surgery has been recently debated for good responders. Therefore, a de-escalation strategy, by omitting radical surgery in good responders, has recently been developed with two different options a watch and wait strategy, requiring an accurate clinical and radiological definition of complete response and a local excision strategy including patients with sub-complete response. Ongoing trials focus on response optimization by chemotherapy intensification or radiotherapy dose escalation. However, many questions are still to be answered regarding definition of complete response, follow-up strategy, morbidity of salvage surgery in case of recurrence as well as long-term oncological and functionnal results.Following rapid environmental change, why do some animals thrive, while others struggle? We present an expanded, cue-response framework for predicting variation in behavioral responses to novel situations. We show how signal detection theory can be used when individuals have three behavioral options (approach, avoid, or ignore). Based on this theory, we outline predictions about which animals are more likely to make mistakes around novel conditions (i.e., fall for a trap or fail to use an undervalued resource) and the intensity of that mismatch (i.e., severe versus moderate). Explicitly considering three options provides a more holistic perspective and allows us to distinguish between severe and moderate traps, which could guide management strategies in a changing world.
To reveal the molecular mechanism of anti-angiogenic activity of semisynthetic triterpenoid CDDO-Im.

Using re-analysis of cDNA microarray data of CDDO-Im-treated human vascular endothelial cells (HUVECs) (GSE71622), functional annotation of revealed differentially expressed genes (DEGs) and analysis of their co-expression, the key processes induced by CDDO-Im in HUVECs were identified. Venn diagram analysis was further performed to reveal the common DEGs, i.e. genes both susceptible to CDDO-Im and involved in the regulation of angiogenesis. A list of probable protein targets of CDDO-Im was prepared based on Connectivity Map/cheminformatics analysis and chemical proteomics data, among which the proteins that were most associated with the angiogenesis-related regulome were identified. Finally, identified targets were validated by molecular docking and text mining approaches.

The effect of CDDO-Im in HUVECs can be divided into two main phases the short early phase (0.5-3h) with an acute FOXD1/CEBPA/JUNB-retion.Colorectal cancer (CRC) is one of the common types of cancer with a high mortality rate. Colonoscopy is the gold standard for CRC screening and significantly reduces CRC mortality. However, due to many factors, the rate of missed polyps, which are the precursors of colorectal cancer, is high in practice. Therefore, many artificial intelligence-based computer-aided diagnostic systems have been presented to increase the detection rate of missed polyps. In this article, we present deep learning-based methods for reliable computer-assisted polyp detection. The proposed methods differ from state-of-the-art methods as follows. First, we improved the performances of YOLOv3 and YOLOv4 object detection algorithms by integrating Cross Stage Partial Network (CSPNet) for real-time and high-performance automatic polyp detection. Then, we utilized advanced data augmentation techniques and transfer learning to improve the performance of polyp detection. Next, for further improving the performance of polyp detection using negative samples, we substituted the Sigmoid-weighted Linear Unit (SiLU) activation functions instead of the Leaky ReLU and Mish activation functions, and Complete Intersection over Union (CIoU) as the loss function. In addition, we present a comparative analysis of these activation functions for polyp detection. We applied the proposed methods on the recently published novel datasets, which are the SUN polyp database and the PICCOLO database. Additionally, we investigated the proposed models for MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy dataset. The proposed methods outperformed the other studies in both real-time performance and polyp detection accuracy.Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians.Cyperus rotundus L. is used to treat multiple clinical conditions like inflammation, diarrhea, pyrosis, and metabolic disorders including diabetes and obesity. The present study aimed to predict the interaction of reported bioactives from Cyperus rotundus against obesity via network pharmacology and to evaluate the efficacy of hydroalcoholic extract of Cyperus rotundus against the olanzapine-induced weight gain and metabolic disturbances in experimental animals. Reported phytochemicals of Cyperus rotundus were retrieved from the open-source database(s) and published literature and their targets were predicted using SwissTargetPrediction, enriched in STRING, and bioactives-proteins-pathways network was constructed using Cytoscape. Further, the hydroalcoholic extract of Cyperus rotundus (100, 200, and 400 mg/kg/day, p.o.) was co-administered with olanzapine (2 mg/kg, i.p.) for 21 days in Sprague Dawley rats. Brefeldin A During treatment, body weight and food intake were recorded; after the successful completion of 21 days of treatment, animals were fasted to perform oral glucose and insulin tolerance tests. Further, the animals were euthanized; blood and abdominal fat were collected for lipid profiling and histopathological examination respectively. Herein, network pharmacology predicted neuroactive ligand-receptor interaction as a primarily modulated pathway and protein tyrosine phosphatase 1b as a majorly triggered protein via the combined action of bioactives. Further, Cyperus rotundus significantly reversed weight gain, cumulative food intake, ameliorated the lipid and glucose metabolism, and promoted energy expenditure.Because an augmented-reality-based brain-computer interface (AR-BCI) is easily disturbed by external factors, the traditional electroencephalograph (EEG) classification algorithms fail to meet the real-time processing requirements with a large number of stimulus targets or in a real environment. We propose a multi-target fast classification method for augmented-reality-based steady-state visual evoked potential (AR-SSVEP), using a convolutional neural network (CNN). To explore the availability and accuracy of high-efficiency multi-target classification methods in AR-SSVEP with a short stimulation duration, a similar stimulus layout was used for a computer screen (PC) and an optical see-through head-mounted display (OST-HMD) device (HoloLens). The experiment included nine flicker stimuli of different frequencies, and a multi-target fast classification method based on a CNN was constructed to complete nine classification tasks, for which the average accuracy of AR-BCI in our CNN model at 0.5- and 1-s stimulus duration was 67.93% and 80.83%, respectively. These results verified the efficacy of the proposed model for processing multi-target classification in AR-BCI.
For the followers of criminal anthropology, during the second half of the 19th and the beginning of the 20th century, the association "anatomical anomaly-psyche anomaly" represented an immediate diagnostic tool to identify mental illness and consequently the tendency to become a criminal. In this article, we analyse a clinical report published in 1900 in which the author, Dr. Saporito, described five brains of alienated criminals from the Aversa asylum.

Through the observations of Dr. Saporito's autoptic evaluations and the literature of the times, the beliefs of the positivist science of that time are highlighted.

The identification of multiple physical anomalies focused on the brains, with particular attention to the alteration at the level of some fissures, could lead to identify psychiatric disorders and criminal tendency.

From the observations presented here, the author reiterated that several anomalies recorded in these five brains reproduced atavistic characteristics, which disappeared in the ontogenetic and phylogenetic evolution of the human brain.
From the observations presented here, the author reiterated that several anomalies recorded in these five brains reproduced atavistic characteristics, which disappeared in the ontogenetic and phylogenetic evolution of the human brain.
The coronavirus disease (COVID-19) pandemic has caused a major health crisis and the quarantine of most of the planet's population. During confinement, anxiety symptoms may appear. The pandemic dramatically changes the lives of individuals by becoming a concrete manifestation of the threat. Constant exposure to information about the virus can increase anxiety, especially since the information may be erroneous or contradictory. This article examines the factors that predict student anxiety in the context of a pandemic.

The quantitative study involves a sample of 445 students from the University of Quebec in Abitibi-Témiscamingue. Anxiety was measured using the Beck Anxiety Inventory, and several sociodemographic variables were tested.

The results demonstrated the effects of certain variables on anxiety, especially for women and non-binary people, were more marked than for men. Having dependent children has proven to be a protective factor.

The study suggests that this variability is now considered when proposing intervention measures in a containment context. The limitations and perspectives of the study are presented and analyzed.
The study suggests that this variability is now considered when proposing intervention measures in a containment context. The limitations and perspectives of the study are presented and analyzed.
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