Objective The mean platelet volume (MPV) is a measure of platelet size, and it is considered a surrogate marker of platelet activation. Because the correlation between platelet count/size and lung cancer prognosis remains unclear, this meta-analysis comprehensively evaluated the prognostic significance of MPV among patients with lung cancer. Methods A systematic search of PubMed, Embase, Google Scholar, and additional sources of relevant studies were conducted with no language restrictions from inception to 7 May 2021. Overall survival (OS) and disease-free survival (DFS)/progression-free survival (PFS), as well as their hazard ratios (HR) and 95% confidence intervals (CIs), were pooled to evaluate the relationship between MPV and survival. The study protocol was registered on PROSPERO. Results Eleven studies involving 2421 patients with lung cancer were included in our analysis. Nine studies including only patients with non-small cell lung cancer were included in the meta-analysis. Our analysis revealed no significant associations of MPV with OS (HR = 1.09, 95% CI = 0.84–1.41) and DFS/PFS (HR = 1.13, 95% CI = 0.58–2.20). Conclusion Pretreatment MPV levels did not display prognostic significance in patients with NSCLC. Large-scale prospective studies and a validation study considering ethnicity and lung cancer staging are warranted.

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Functional near infrared spectroscopy (fNIRS) was used to explore hemodynamic responses in the human frontal cortex to noxious thermal stimulation over the right temporomandibular joint (TMJ). fNIRS experiments were performed on nine healthy volunteers under both low‐pain stimulation (LPS) and high‐pain stimulation (HPS), using a temperature‐controlled thermal stimulator. By analyzing the temporal profiles of changes in oxy‐hemoglobin concentration (HbO) using cluster‐based statistical tests, several regions of interest in the prefrontal cortex, such as the dorsolateral prefrontal cortex and the anterior prefrontal cortex, were identified, where significant differences (p < .05) between HbO responses to LPS and HPS were shown. In order to classify these two levels of pain, a neural network‐based classification algorithm was utilized. With leave‐one‐out cross‐validation, the two levels of pain were identified with 99% mean accuracy to high pain. Furthermore, the “internal mentation hypothesis” and the default‐mode network were introduced to explain our observations of the contrasting trend, as well as the rise and fall of HbO responses to HPS and LPS.

are analyzed. The use of optimal diagonal transformation matrices on the net function vector is proved to be equivalent to training the MLP using multiple optimal learning factors (MOLF). A method for linearly compressing large ill-conditioned MOLF Hessian matrices into smaller wellconditioned ones is developed. This compression approach is shown to be equivalent to using several hidden units per learning factor. The technique is extended to large networks. In simulations, the proposed algorithm performs almost as well as the Levenberg Marquardt (LM) algorithm with the computational complexity of a first order training algorithm.

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