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Objective To investigate the association of inflammatory and nutritional indicators with the efficacy of neoadjuvant chemotherapy (NACT) in patients with upper gastric cancer before and after NACT, and to establish a clinical predictive model. Methods A total of 117 patients with upper gastric cancer who underwent NACT in Department of Gastroenterology in our hospital from April 2013 to January 2022 were enrolled, and according to the efficacy of chemotherapy, they were divided into effective group and ineffective group. Related clinical data were collected, such as age, sex, smoking history, drinking history, initial symptoms, tumor location, degree of tumor differentiation, tumor stage, tumor pathological type, and routine blood test results within one week before NACT and within one week after NACT. Univariate and multivariate analyses were performed to identify the influencing factors for the efficacy of NACT, and then a nomogram model was established and validated. Results The multivariate analysis showed that the difference in plasma neutrophil-to-lymphocyte ratio before and after NACT (△NLR) (OR=2.043,95%CI=1.334-3.127,P

Environmental factors and loads cause potential small cracks in the rubber layers of laminated bearings. Conventional methods for identifying structural damage based on global vibration characteristics are ineffective, and current nondestructive structural damage detection methods based on Piezoelectric ceramic lead Zirconate Titanate (PZT) cannot fully capture deep damage-sensitive features from PZT signals. This study proposes a novel deep learning approach using a convolutional neural network with residual connections to conduct raw electromechanical admittance analysis and automatically extract damage-sensitive features to identify minor cracks in laminated rubber bearings. Numerical models are used to generate a sufficient amount of PZT data under various damage conditions for training the deep neural network, which overcomes the sparsity of damage states in actual laminated rubber bearings. A transfer learning method based on a model fine-tuning strategy was adopted to address the feature differences between the numerical models and actual rubber bearing structure. The fine-tuned model could effectively identify the damage location of actual laminated rubber bearings. The proposed method does not require a complex data preprocessing process when analyzing PZT signals and has a relatively high accuracy in damage localization. The promising results from this work offer a potential paradigm for data-driven PZT signal analysis, which is an appealing approach for identifying minor damage in real-life laminated rubber bearings. [ABSTRACT FROM AUTHOR]