Objective To develop a deep learning algorithm(Bio-section to Gene,B2G)for predicting spatially resolved gene expression profiles directly from histopathological images.Methods Digital histopathological images were processed through an integrated framework comprising convolutional neural networks(CNNs)and the Transformer architecture.Local histological features were extracted by the CNN module while global feature correlations were captured by the Transformer module.Cellular characteristics in digital tissue sections were systematically identified,followed by regression-based prediction of spatially resolved gene expression profiles.Results The B2G algorithm demonstrated significantly higher prediction accuracy than existing methods(weighted median PCC 0.1776).This framework exhibited robust performance across multiple cancer types and histological preparation protocols.Conclusion This computational approach may provide a morphology-driven strategy for spatial transcriptomic analysis.The framework could facilitate cost-effective biomarker discovery in clinical specimens while reducing reliance on specialized molecular techniques.Additionally,it might enable further exploration of tumor microenvironment heterogeneity.

Objective To understand the ability and level of emergency rescue at general hospitals in Tianjin city. Methods Such actions as formulating plans and examination forms, establishing assessment indicators and evaluation criteria, and simulation exercises were performed to evaluate the capacity of 28 general hospitals in terms of their organizational structure, emergency response, event reporting, and summary assessment. Results The emergency response assessment system consisted of 4 level-1 indicators, 19 level-2 indicators and 58 level-3 indicators. 28 hospitals were found high in their overall emergency response capacity, but some were found with setbacks. For example, the " organizational structure" scored the highest in 4 first-level indicators, up to 88. 91％ , while " incident report" scored the lowest, down to 67. 99％ . Among level-2 indicators, professional emergency professional procedures and initial reporting scored the lowest. Conclusions In order to further improve the ability of medical institutions to respond to emergency events, the hospitals are recommended to enhance their backup resources for emergency response, their staff′s awareness of first aid knowledge and first aid skills, as well as their timeliness of initial reports and the completeness of progress reports.

Objective To investigate the diagnostic value comparatively of CT and radiography in pneumoconiosis.Methods Chest radiographic and CT images were analyzed retrospectively in 52 cases with pneumoconiosis.Results The pulmonary disseminated small shadowes (diameter 10 mm),13 cases and 9 cases were detected by CT and radiography respectively.20 cases (5 pneumonia,3 pulmonary tuberculosis,1 lung cancer,6 pneumothorax,4 pulmonary emphysema) and 10 cases (1 pneumonia,1 pulmonary tuberculosis,6 pneumothorax,2 pulmonary emphysema) with complications were showed by CT and radiography respectively.Conclusion CT is not superior to radiography in diagnosis of simple pneumoconiosis,but CT is superior to radiography detecting the big shadow of lung and complications,and it can help radiologists to avoid mistakes.