Abstract The neutrophil-to-lymphocyte ratio is used to reflect body's inflammatory status with prognostic value in different cancers. We aimed to investigate the influence of preoperative NLR in the prognosis of CRLM patients receiving surgery using meta-analysis. Data in Cochrane Library, PubMed, Embase, and Web of Science databases created before October 2022 were recruited. Meta-analysis was carried out with RevMan 5.3 and Stata16 software, and the primary outcome indicators included overall survival (OS), and secondary outcome indicators included disease-free survival (DFS) and relapse-free survival (RFS). The pooled risk ratio (HR) and 95% confidence interval (CI) for each outcome indicator were determined using random-effects models or fixed-effects models. The pooled odds ratio (OR) and corresponding 95% confidence intervals (CI) for NLR and clinicopathological characteristics were determined with a fixed-effects model. 18 papers published between 2008 and 2022 (3184 patients in total) were included. The pooled analysis found that high preoperative NLR was correlated with poor OS (multivariate HR = 1.83, 95% CI = 1.61–2.08, p < 0.01), DFS (multivariate HR = 1.78, 95% CI = 1.16–2.71, p < 0.01) and RFS (multivariate HR = 1.46, 95% CI = 1.15–1.85, p < 0.01), but NLR was not related to clinicopathological features of CRLM patients correlation. In conclusion, NLR is an independent risk factor for poor prognosis in patients with CRLM. More large-scale clinical researches are required in the future to demonstrate the inclusion of preoperative NLR as a prognostic indicator for CRLM patients to guide postoperative adjuvant chemotherapy.
Dendritic cell (DC) vaccines are currently one of the most effective approaches to treat melanoma. The immunogenicity of antigens loaded into DCs determines the treatment effects. Patients treated with autologous antigen-loaded DC vaccines achieve the best therapeutic effects. In China, most melanoma patients cannot access their autologous antigens because of formalin treatment of tumor tissue after surgery. In the present study, we purified heat shock protein 70 (HSP70)-peptide complexes (PCs) from human melanoma cell lines A375, A875, M21, M14, WM‑35, and SK‑HEL‑1. We named the purified product as M‑HSP70‑PCs, and determined its immunological activities. Autologous HSP70‑PCs purified from primary tumor cells of melanoma patients (nine cases) were used as controls. These two kinds of tumor antigenic complexes loaded into DCs were used to stimulate an antitumor response against tumor cells in the corresponding patients. Mature DCs pulsed with M‑HSP70‑PCs stimulated autologous T cells to secrete the same levels of type I cytokines compared with the autologous HSP70‑PCs. Moreover, DCs pulsed with M‑HSP70‑PCs induced CD8+ T cells with an equal ability to kill melanoma cells from patients compared with autologous HSP70‑PCs. Next, we used these PC‑pulsed autologous DCs and induced autologous specific CD8+ T cells to treat one patient with melanoma of the nasal skin and lung metastasis. The treatment achieved a good effect after six cycles. These findings provide a new direction for DC-based immunotherapy for melanoma patients who cannot access autologous antigens.
Objective:To evaluate the clinical value of TPS,CYFRA21-1 and CEA in the diagnosis of varied groups of lung cancer.To observe the sensitivity and accuracy by combined detection of three tumor markers.Methods:The serum samples of 127 patients were measured:including 22 squamous cell carcinomas,19 adenocarcinomas ,16 small-cell cancers,13 unclassified lung cancers,27 benign pulmonary diseases and 30 normal control groups.Serum was e dtected by ELISA.Results:There was a difference in serum level of TPS,CYFRA21-1 and CEA(P0 05) between lung cancer groups and control groups.The serum level of TPS and SCLC is higher in lung cancer group(P0 05) than that in benign group.There was no difference(P0 05)in the serum level of CYFRA21-1between lung cancer groups and benign group.The sensitivity of TPS of 85.7% in lung cancer groups is the highest and it is 29.6% in benign groups and the specificity is 78.9%.There was no difference in sensitivity of TPS in varied lung cancer groups(P0 05).The sensitivity of CYFRA21-1 and CEA is,respectively 27.1% and 22.8%.CYFRA21-1 shwed the highest sensitivity (36.4%) in squamous cell carcionmas,CEA highest in adenocarcinomas (31.6%).The combined detection of three tumor markers may improve the sensitivity for lung cancer detection,but specificity declines.Conclusion:Being the tumor markers with high specificity and sensitivity,TPS has a higher sensitivity in the detection of lung cancer but shows low specificity,thus can not be used alone in the detection of lung cancer.CYFR21-1 and CEA has a lower sensitivity but high specificity,thus shows some clinical value.The combined detection of three tumor markers can increase diagnosis rate of lung cancer,which shows some clinical value.
In time-sensitive Internet of Things (IoT) systems which is assisted by Unmanned Aerial Vehicle (UA V), most of the existing researches focus on maximizing the system throughput or minimizing the delay, but these researches has ignored the timeliness of the information at the receiver. This paper proposes a UAV-assisted IoT fresh information collection system model, and studies the information collection process and information freshness in the system. In this system, the base station sends the UAV to fly to the sensor node and collect the data in the sensor node. In the process of data collection, the energy of the UAV must be kept in the surplus state. Once the energy of the UAV is lower than the energy threshold, the UAV immediately flies to the destination. The data collected from the sensor node is stored in the buffer and follows the proposed queuing policy which replace old packets with new packets. In this paper, the problem is modeled as a Markov Decision Process and the state space, action space and reward function of the problem are defined. The system average age of information is minimized by jointly optimizing the flight trajectory of UAV and the transmission scheduling sequence of sensors. In order to overcome the disaster of dimension, this paper proposes a node data collection algorithm based on Double Deep Q Learing(DDQN). A large number of simulation experiments show that the proposed DDQN algorithm can reduce the system average age of information effectively compared with other baseline algorithms.
The present researches of the MIMO theory only proved that channel capacity (CC) could be increased on the setting mathematical model in base-band by the space division multiplex (SDM) principle.But the reasonable physical model matching to that mathematical model has not been given by those researches, resulting in that the SDM could not be realized in MIMO system.So that the CC formula of MIMO system deduced by that mathematical model could not be realized physically.The infeasibility of MIMO system would be discussed in the paper in the aspects of physical characteristic of antenna, radio beam patterns of many kinds of antenna array, the un-reasonability of the MIMO physical model, the experiments of MIMO, and etc.
In order to overcome the problem that cloud storage is not trusted and the low efficiency of ciphertext retrieval in cloud storage, a searchable ciphertext sorting encryption scheme based on B+ tree on the block chain is proposed. Combined with the blockchain technology, the problem of establishing reliable trust in multiple parties that do not understand each other is solved. A vector space model is used to reduce the complexity of the text and an efficient text retrieval system is implemented. The index structure of the B+ tree is used to improve the retrieval of ciphertext transactions on the blockchain. The ranking of multi-keyword query results is realized by the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm. Under the random oracle model, it is proved that the scheme is adaptive and indistinguishable. Through the comparative analysis of efficiency, it is shown that the scheme achieves efficient ciphertext retrieval on the blockchain.
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