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This study aimed to evaluate the diagnostic efficacy of seven autoantibodies in all lung cancer, lung adenocarcinoma, lung squamous cell carcinoma and early-stage lung cancer patients. ELISA testing of a seven autoantibody panel was performed on 386 lung cancer patients and 238 normal controls. The sensitivity and specificity of each autoantibody were analyzed using the receiver operating characteristic curve analysis. The diagnostic efficacy of a combination of these seven autoantibodies was evaluated by binary logistic regression. The results indicated that six of the seven autoantibodies (p53, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE) had high specificity and low sensitivity, while PGP9.5 had high sensitivity and low specificity. Further analysis showed that all seven autoantibodies had better diagnostic value in lung squamous cell carcinoma patients when compared to lung adenocarcinoma or all lung cancer patients. Logistic regression showed that a combination of the seven autoantibodies resulted in more reliable detection of lung cancer than any individual autoantibody in early-stage lung cancer (sensitivity/specificity: 47.8%/81.4%, areas under the curve: 0.764, 95% confidence interval: 0.718–0.811). Additionally, this panel had a better sensitivity of 56.5% for detection of lung squamous cell carcinoma than for all lung cancer (50.1%) or adenocarcinoma (51.7%) (P < 0.05). Our results indicated that the seven autoantibody panel could be used for early lung cancer detection, and it had better sensitivity in diagnosis of lung squamous cell carcinoma.

In order to relieve the problem of unbalanced energy consumption of sensor nodes near the base station in the wireless sensor network, this paper proposes a mobile multi-sink nodes path planning algorithm with energy balance (hexHPSO). An optimization model is established by considering the energy consumption of each group, network lifetime, and movement path of the mobile sink nodes. Meanwhile, a hybrid positive and negative particle swarm optimization algorithm (HPNPSOA) is proposed to solve the optimization model to obtain a path with optimal grid traversal order and optimal parking position. Compared with the DOSM algorithm, GLRM algorithm, and RWM algorithm, the hexHPSO algorithm improves the network lifetime by 68%. The experimental results show that the hexHPSO algorithm can effectively balance the energy consumption, alleviate hotspot phenomenon, and extend the network lifetime. INDEX TERMS Wireless sensor networks, mobile multi-sink nodes, path planning, hybrid positive and negative particle swarm optimization, network lifetime.

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