Background: Pemetrexed/platinum chemotherapy has been the standard chemotherapy regimen for lung adenocarcinoma patients, but the efficacy varies considerably. Methods: To discover new serum biomarkers to predict the efficacy of pemetrexed/platinum chemotherapy, we analyzed 20 serum samples from advanced lung adenocarcinoma patients who received pemetrexed/platinum chemotherapy with the data-independent acquisition (DIA) quantitative mass spectrometry (MS). Results: The 20 patients were categorized as “good response” [12 patients achieving partial response (PR)] and “poor response” [8 patients with progressive disease (PD)] groups. Altogether 23 significantly different expressed proteins were identified, which had relative ratios higher than 1.2 or lower than –0.83, with 7 proteins having an area under the curve (AUC) above 0.8. To further validate the DIA results, we used the parallel reaction monitoring (PRM) method to examine 16 candidate serum biomarkers in the study cohort of 20 patients and another cohort of 22 advanced lung adenocarcinoma patients (16 PR and 6 PD). Quantitative validation using PRM correlated well with the DIA results, and 10 promising proteins exhibited a similar up- or downregulation. It is worth noting that glutathione peroxidase 3 (GPX3) exhibits significant upregulation in the poor response group compared with the good response group, which was validated by both DIA and PRM methods. Conclusions: Our study confirmed that combined DIA MS and PRM approaches were effective in identifying serum predictive biomarkers for advanced lung adenocarcinoma patients. Further studies are needed to explore the potential biological mechanism underlying these biomarkers.

This paper presents a new electro-thermal coupling simulation method for evaluating the reliability of IGBT modules, which combines a numerical power loss model and a finite element model. To illustrate the method, a specific case of the Infineon FF50R12RT4 module operated with an SPWM signal is considered. Temperature and stress data are obtained and analyzed via electro-thermal simulation, and the service life of modules is calculated accordingly. The influence of ambient temperature and gate signal characteristics on power loss and service life of IGBT modules is discussed, which can be used for the optimization of work points. In this method, the effect of junction temperature on power loss is considered, and a more accurate electro-thermal simulation is achieved. The proposed method can be used to analyze the thermal and mechanical stresses of IGBT modules, predict the failure location, and assess the service life.

Many unsupervised domain adaptation methods leverage pseudo-labels to facilitate model training in the target domain. However, due to the domain gap, these pseudo-labels often exhibit a certain level of noise, leading to a dispersion of intra-class features within the target domain. Unlike previous self-training methods that use random perturbations for consistency regularization, we propose an adversarial attack consistency framework for unsupervised domain adaptation, which treats the adversarial example as a different version of the original sample in the target domain and aligns the predictions between them. The algorithm effectively facilitates the network's ability to learn class-wise semantic information and a more compact feature space in the target domain. The experimental results show that the proposed algorithm has achieved significant performance over state-of-the-art methods.