Correlation between CD4, CD8 cell infiltration in gastric mucosa, Helicobacter pylori infection and symptoms in patients with chronic gastritis

AIM: To evaluate the correlation between CD4, CD8 cell infiltration in gastric mucosa, Helicobacter pylori(H pylori)infection and symptoms or the assemblage of symptoms in cases with chronic gastritis.METHODS: Biopsy samples at the gastric antrum were obtained from 62 patients with chronic gastritis. CD4 and CD8 cell infiltration was evaluated by immunohistochemical assays on frozen sections of the biopsy samples. Fifteen symptoms referring to digestion-related activity and nondigestion related activity were observed. The correlation between lymphocyte infiltration and each symptom or symptom assemblage was analyzed by logistic regression and K-mean cluster methods.RESULTS: CD4 cell infiltrations in gastric mucosa were much more in patients with H pylori infection, while CD8 cell infiltrations were similar in patients with or without H pylori infection. Logistic regression analysis showed that the symptoms including heavy feeling in head or body (t= 2.563), and thirst (t= 2.478) were significantly related with CD4 cell infiltration in gastric mucosa (P＜0.05), and cool limbs with aversion to cold were related with CD8cell infiltration (t = 2.872, P＜0.05). Further analysis showed that non-digestive related symptom assemblage could increase the predicted percentage of CD4 and CD8cell infiltration in gastric mucosa, including lower CD4infiltration by 12.5%, higher CD8 infiltration by 33.3%,and also non-H pylori infection by 23.6%.K-means cluster analysis of all symptoms and CD4 and CD8 cell infiltration in gastric mucosa showed a similar tendency to increase the predicted percentage of CD4, CD8 cell infiltration and H pylori infection.CONCLUSION: Based on correlation between the gastric mucosa lymphocyte infiltration, H pylori infection and clinical symptoms, symptoms or symptomatic assemblages play an important role in making further classification of chronic gastritis, which might help find a more specific therapy for chronic gastritis.

CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.