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Objective To investigate the relation between TMB and the efficiency of PD-1/PD-L1 inhibitors treatment for non-small-cell lung cancer. Methods Studies were searched from PubMed, Embase, Cochrane Library database, Chinese Biomedical Literature Database and Wanfang Database up to March 25, 2020. RevMan 5.3 software and STATA15.0 were used for analysis. Results Twelve literatures were involved, including 1209 patients. TMB significantly improved PFS (HR=0.54, 95%CI: 0.42-0.70, P < 0.001) but reduced the ORR (OR=4.41, 95%CI: 2.54-7.63, P < 0.001) of NSCLC patients treated with PD-1/PD-L1 inhibitors. The subgroup analyses showed that the predictive value of TMB was significant in non-small cell lung cancer treated by PD-1/PD-L1 inhibitors combined with anti-CTLA-4 therapy or chemotherapy. No significant publication bias was observed by the Begg's test and Egger's test. Conclusion High tumor mutation burden may predict the improved PFS of non-small cell lung cancer by PD-1/PD-L1 inhibitors treatment, but its predictive value for OS, ORR and long-term survival need more exploration.

A kind of underground adaptive positioning algorithm based on PSO-BP neural network was proposed. In view of the problem that traditional positioning algorithm based on ranging model is sensitive to coal mine environment disturbance and ranging error is large, a fingerprint matching positioning model is selected for positioning. In view of the problem that strong time-varying nature of coal mine environment is easy to increase matching error between the fingerprint information collected in real time and the static fingerprint database information established in offline phase, the beacon node is used as calibration node to better reflect the condition of reference point changes with environment, and avoid adding additional calibration nodes. The dynamic compensation method is used to correct the fingerprint data of the target node in real time without increasing the hardware cost, which solves the problem of poor adaptation of the fingerprint matching positioning model. At the matching positioning stage, PSO is used to optimize weight of BP neural network to accelerate convergence of BP neural network and improve learning speed. The experimental results show that the algorithm is more adapted to the coal mine environment varies with time, and meets the requirement of adaptive underground positioning.

Serine is a functional amino acid that effectively regulates the physiological functions of an organism. This study investigates the effects of adding exogenous serine to a culture medium to explore a feasible method for the rejuvenation of V. volvacea degenerated strains. The tissue isolation subcultured strains T6, T12, and T19 of V. volvacea were used as test strains, and the commercially cultivated strain V844 (T0) was used as a control. The results revealed that the addition of serine had no significant effect on non-degenerated strains T0 and T6, but could effectively restore the production characteristics of degenerated strains T12 and T19. Serine increased the biological efficiency of T12 and even helped the severely degenerated T19 to regrow its fruiting body. Moreover, exogenous serine up-regulated the expression of some antioxidant enzyme genes, improved antioxidase activity, reduced the accumulation of reactive oxygen species (ROS), lowered malondialdehyde (MDA) content, and restored mitochondrial membrane potential (MMP) and mitochondrial morphology. Meanwhile, serine treatment increased lignocellulase and mycelial energy levels. These findings form a theoretical basis and technical support for the rejuvenation of V. volvacea degenerated strains and other edible fungi. [ABSTRACT FROM AUTHOR]

This work aims to explore the Lower Silurian shale gas and tight sandstone gas accumulation conditions in the Gongtan Syncline, southeastern Sichuan Basin. The sedimentary environment, organic geochemical characteristics, reservoir characteristics, gas content, and preservation conditions of the reservoir were comprehensively analyzed. The results show that the Wufeng–Longmaxi Formation shale formed in a deep-water shelf characterized by a large thickness (50–70 m), appropriate total organic carbon content (0.5–5.47%), high maturity (2.38%), high brittle mineral content (67.10%), and large gas content (0.71–1.64 m3/t), and the formations show the good resource potential of the shale gas. The Xintan Formation formed in a lower shore phase, and the tight sandstone is locally developed with a small thickness. The Xiaoheba Formation formed in an upper-middle shore phase, and the tight sandstone is stably distributed with large thicknesses. The porosity and permeability of the two sets of sandstone are small and some natural fractures are developed in the sandstone, but the fracture filling degree is higher. The results of well logging show that there are abnormally high values of total hydrocarbon in both the Xintan Formation and Xiaoheba Formation; this indicates that tight sandstone gas is developed in the Lower Silurian strata. A comprehensive study indicates that the Lower Silurian of the Gongtan Syncline has the geological conditions for the formation of shale gas and tight sandstone gas, which are the "Two gases" with good co-exploration prospects. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human–AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research. [ABSTRACT FROM AUTHOR]

Radiotherapy is an important treatment for many unresectable advanced malignant tumors, and radiotherapy-associated inflammatory reactions to radiation and other toxic side effects are significant reasons which reduce the quality of life and survival of patients. FLASH-radiotherapy (FLASH-RT), a prominent topic in recent radiation therapy research, is an ultra-high dose rate treatment known for significantly reducing therapy time while effectively targeting tumors. This approach minimizes radiation side effects on at-risk organs and maximally protects surrounding healthy tissues. Despite decades of preclinical exploration and some notable achievements, the mechanisms behind FLASH effects remain debated. Standardization is still required for the type of FLASH-RT rays and dose patterns. This review addresses the current state of FLASH-RT research, summarizing the biological mechanisms behind the FLASH effect. Additionally, it examines the impact of FLASH-RT on immune cells, cytokines, and the tumor immune microenvironment. Lastly, this review will discuss beam characteristics, potential clinical applications, and the relevance and applicability of FLASH-RT in treating advanced cancers. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the potential benefit of blood inflammation in the diagnosis of non-small cell lung cancer (NSCLC) and propose a machine-learning-based method to predict NSCLC in asymptomatic adults. Patients and Methods: A cross-sectional study was evaluated using medical records of 139 patients with non-small cell lung cancer and physical examination data from May 2022 to May 2023 of 198 healthy controls. The NSCLC cohort comprised 128 cases of adenocarcinoma, 3 cases of squamous cell carcinoma, and 8 cases of other NSCLC subtypes. The correlation between inflammatory and nutritional markers, such as monocytes, neutrophils, LMR, NLR, PLR, PHR and non-small cell lung cancer was examined. Features were selected using Python's feature selection library and analyzed by five algorithms. The predictive ability of the model for non-small cell lung cancer diagnosis was assessed by precision, accuracy, recall, F1 score, and area under the curve (AUC). Results: The results showed that the top 14 important factors were PDW, age, TP, RBC, HGB, LYM, LYM%, RDW, PLR, LMR, PHR, MONO, MONO%, gender. Additionally, the naive Bayes (NB) algorithm demonstrated the highest overall performance in predicting adult NSCLC among the five machine learning algorithms, achieving an accuracy of 0.87, a macro average F1 score of 0.85, a weighted average F1 score of 0.87, and an AUC of 0.84. Conclusion: In feature ranking, platelet distribution width was the most important feature, and the NB algorithm performed best in predicting adult NSCLC diagnosis. [ABSTRACT FROM AUTHOR]