Metabolic alterations in patients with Helicobacter pylori-related gastritis: The H. pylori-gut microbiota-metabolism axis in progression of the chronic inflammation in the gastric mucosa.

PURPOSE: To characterize the serum metabolism in patients with Helicobacter pylori-positive and H. pylori-negative gastritis. METHODS: Clinical data and serum gastric function parameters, PGI (pepsinogen I), PGII, PGR (PGI/II), and G-17 (gastrin-17) of 117 patients with chronic gastritis were collected, including 57 H. pylori positive and 60 H. pylori negative subjects. Twenty cases in each group were randomly selected to collect intestinal mucosa specimens and serum samples. The gut microbiota profiles were generated by 16S rRNA gene sequencing, and the serum metabolites were analyzed by a targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) technology. RESULTS: Altered expression of 20 metabolites, including isovaleric acid, was detected in patients with HPAG. Some taxa of Bacteroides, Fusobacterium, and Prevotella in the gut microbiota showed significant correlations with differentially expressed metabolites between H. pylori positive and H. pylori negative individuals. As a result, an H. pylori-gut microbiota-metabolism (HGM) axis was proposed. CONCLUSION: Helicobacter pylori infection may influence the progression of mucosal diseases and the emergence of other complications in the host by altering the gut microbiota, and thus affecting the host serum metabolism.

3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.

Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.

Impact of Paxlovid on in-hospital outcomes and post-COVID-19 condition in adult patients infected with SARS-CoV-2 Omicron variant: A non-randomized controlled clinical trial.

BACKGROUND: Nirmatrelvir plus ritonavir (Paxlovid) have been used in the treatment of adult patients with mild-to-moderate coronavirus disease 2019 (COVID-19). This study aimed to evaluate the impact of Paxlovid on in-hospital outcomes and post-COVID-19 condition in Chinese adult patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. METHODS: This non-randomized clinical controlled trial recruited patients infected with SARS-CoV-2 Omicron variant from the designated hospital for treating COVID-19 between November 5 and November 28, 2022, in Shijiazhuang, China. Participants were administered Paxlovid (300 mg of nirmatrelvir and 100 mg of ritonavir orally) or standard treatment. The primary outcome was the nucleic acid shedding time and post-COVID-19 condition. RESULTS: A total of 320 patients infected with SARS-CoV-2 Omicron variant were included, with mean age of 29.10 ±â€…7.34 years old. Two hundred patients received Paxlovid. Compared to patients in the standard treatment group, those in Paxlovid group had a significantly shorter nucleic acid shedding time (3.26 ±â€…1.80 vs 7.75 ±â€…3.68 days, P < .001), shorter days until negative swab test (1.74 ±â€…1.15 vs 5.33 ±â€…2.91, P < .001), shorter days of first symptoms resolution (4.86 ±â€…1.62 vs 7.45 ±â€…2.63, P < .001), higher in nucleic acid test negative rate within 3 days [138 (70.77%) vs 14 (11.67%), P < .001], higher negative rate within 5 days [174 (89.23%) vs 26 (21.67%), P < .001], negative rate within 7 days [185 (94.87%) vs 78 (65.00%), P < .001], and were less likely to have post-COVID-19 condition [32 (18.60%) vs 30 (31.57%), P = .016]. There was no significant difference in duration of post-COVID-19 condition (43.00 ±â€…26.00 vs 49.00 ±â€…26.34 days, P = .354) between the 2 groups. CONCLUSION: Compared to standard treatment, Paxlovid significantly reduced nucleic acid shedding time, days until negative swab test, and days of first symptoms resolution, as well as improved nucleic acid test negative rate and post-COVID-19 condition.