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Abstract Systemic inflammatory responses caused by tumor cells play an important role in the occurrence and development of tumors. The aim of this study was to identify biomarkers that most accurately predict prognoses in patients with non-metastatic cancer and to evaluate their clinical significance when combined with muscle markers. This study retrospectively evaluated 2,797 cancer patients diagnosed with cancer at TNM stages I, II, and III. Lymphocyte-C-reactive protein ratio (LCR) in conjunction with calf circumference (CC) were used (or chosed) after evaluating the predictive value of 13 inflammatory marker combinations and five anthropometric indicators for patient outcomes using the C-index. The Kaplan–Meier method and Cox’s proportional hazards regression modeling were used to analyze the individual and combined effects of these two potential biomarkers on overall survival. This study enrolled 1,604 men (57.3%) and 1,193 women (42.7%) with a mean age of 58.75 years. Among the 13 inflammatory nutritional indicators, the LCR was the most accurate predictor of prognoses in patients with non-metastatic cancer. After multifactorial adjustment, we found that low LCR had an adverse effect on overall survival (hazard ratio [HR]: 2.50; 95% confidence interval [CI]: 2.17, 2.88; P

Abstract To investigate the prognostic value of systemic inflammation and insulin resistance in women with breast cancer with different body mass index (BMI). This multicenter, prospective study included 514 women with breast cancer. Multivariate survival analysis showed that patients with high C-reactive protein (CRP), high CRP to albumin ratio (CAR), high lymphocyte to CRP ratio (LCR), high low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (LHR), and high triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-c) were significantly associated with worse prognosis. The mortality rate of patients with both high CAR and high LHR or both low LCR and high LHR were 3.91-fold or 3.89-fold higher than patients with both low CAR and low LHR or both high LCR and low LHR, respectively. Furthermore, the combination of LCR and LHR significantly predicted survival in patients within the high BMI group. The CRP, CAR, LCR, LHR, and TG/HDL-c were associated with poor survival in women with breast cancer. The combination of CAR and LHR or LCR and LHR could better predict the prognostic outcomes of women with breast cancer, while the combination of LCR and LHR could better predict the prognosis of those patients with overweight or obese patients.

Abstract To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge from a dynamic simulation model to an actual planetary gearbox. Massive amounts of different fault simulation data are collected by creating a dynamic simulation model of a planetary gearbox. A fresh deep transfer learning network model is built by fusing one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods. The network model is used to learn domain invariant features from simulated data, thereby enabling fault diagnosis on real data. The fault diagnosis experiment is verified by using the Drivetrain Diagnostics Simulator test bench. The validity of the proposed means is evaluated by comparing the diagnostic accuracy of various means on various diagnostic tasks.